MicroFish/backend/app/services/report_agent.py

"""
Report Agent服务
使用LangChain + Zep实现ReACT模式的模拟报告生成

功能：
1. 根据模拟需求和Zep图谱信息生成报告
2. 先规划目录结构，然后分段生成
3. 每段采用ReACT多轮思考与反思模式
4. 支持与用户对话，在对话中自主调用检索工具
"""

import os
import json
import time
import re
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum

from ..config import Config
from ..utils.llm_client import LLMClient
from ..utils.logger import get_logger
from ..utils.locale import get_language_instruction, t
from .zep_tools import (
    ZepToolsService, 
    SearchResult, 
    InsightForgeResult, 
    PanoramaResult,
    InterviewResult
)

logger = get_logger('mirofish.report_agent')


class ReportLogger:
    """
    Report Agent 详细日志记录器
    
    在报告文件夹中生成 agent_log.jsonl 文件，记录每一步详细动作。
    每行是一个完整的 JSON 对象，包含时间戳、动作类型、详细内容等。
    """
    
    def __init__(self, report_id: str):
        """
        初始化日志记录器
        
        Args:
            report_id: 报告ID，用于确定日志文件路径
        """
        self.report_id = report_id
        self.log_file_path = os.path.join(
            Config.UPLOAD_FOLDER, 'reports', report_id, 'agent_log.jsonl'
        )
        self.start_time = datetime.now()
        self._ensure_log_file()
    
    def _ensure_log_file(self):
        """确保日志文件所在目录存在"""
        log_dir = os.path.dirname(self.log_file_path)
        os.makedirs(log_dir, exist_ok=True)
    
    def _get_elapsed_time(self) -> float:
        """获取从开始到现在的耗时（秒）"""
        return (datetime.now() - self.start_time).total_seconds()
    
    def log(
        self, 
        action: str, 
        stage: str,
        details: Dict[str, Any],
        section_title: str = None,
        section_index: int = None
    ):
        """
        记录一条日志
        
        Args:
            action: 动作类型，如 'start', 'tool_call', 'llm_response', 'section_complete' 等
            stage: 当前阶段，如 'planning', 'generating', 'completed'
            details: 详细内容字典，不截断
            section_title: 当前章节标题（可选）
            section_index: 当前章节索引（可选）
        """
        log_entry = {
            "timestamp": datetime.now().isoformat(),
            "elapsed_seconds": round(self._get_elapsed_time(), 2),
            "report_id": self.report_id,
            "action": action,
            "stage": stage,
            "section_title": section_title,
            "section_index": section_index,
            "details": details
        }
        
        # 追加写入 JSONL 文件
        with open(self.log_file_path, 'a', encoding='utf-8') as f:
            f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
    
    def log_start(self, simulation_id: str, graph_id: str, simulation_requirement: str):
        """记录报告生成开始"""
        self.log(
            action="report_start",
            stage="pending",
            details={
                "simulation_id": simulation_id,
                "graph_id": graph_id,
                "simulation_requirement": simulation_requirement,
                "message": t('report.taskStarted')
            }
        )
    
    def log_planning_start(self):
        """记录大纲规划开始"""
        self.log(
            action="planning_start",
            stage="planning",
            details={"message": t('report.planningStart')}
        )
    
    def log_planning_context(self, context: Dict[str, Any]):
        """记录规划时获取的上下文信息"""
        self.log(
            action="planning_context",
            stage="planning",
            details={
                "message": t('report.fetchSimContext'),
                "context": context
            }
        )
    
    def log_planning_complete(self, outline_dict: Dict[str, Any]):
        """记录大纲规划完成"""
        self.log(
            action="planning_complete",
            stage="planning",
            details={
                "message": t('report.planningComplete'),
                "outline": outline_dict
            }
        )
    
    def log_section_start(self, section_title: str, section_index: int):
        """记录章节生成开始"""
        self.log(
            action="section_start",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={"message": t('report.sectionStart', title=section_title)}
        )
    
    def log_react_thought(self, section_title: str, section_index: int, iteration: int, thought: str):
        """记录 ReACT 思考过程"""
        self.log(
            action="react_thought",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "iteration": iteration,
                "thought": thought,
                "message": t('report.reactThought', iteration=iteration)
            }
        )
    
    def log_tool_call(
        self, 
        section_title: str, 
        section_index: int,
        tool_name: str, 
        parameters: Dict[str, Any],
        iteration: int
    ):
        """记录工具调用"""
        self.log(
            action="tool_call",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "iteration": iteration,
                "tool_name": tool_name,
                "parameters": parameters,
                "message": t('report.toolCall', toolName=tool_name)
            }
        )
    
    def log_tool_result(
        self,
        section_title: str,
        section_index: int,
        tool_name: str,
        result: str,
        iteration: int
    ):
        """记录工具调用结果（完整内容，不截断）"""
        self.log(
            action="tool_result",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "iteration": iteration,
                "tool_name": tool_name,
                "result": result,  # 完整结果，不截断
                "result_length": len(result),
                "message": t('report.toolResult', toolName=tool_name)
            }
        )
    
    def log_llm_response(
        self,
        section_title: str,
        section_index: int,
        response: str,
        iteration: int,
        has_tool_calls: bool,
        has_final_answer: bool
    ):
        """记录 LLM 响应（完整内容，不截断）"""
        self.log(
            action="llm_response",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "iteration": iteration,
                "response": response,  # 完整响应，不截断
                "response_length": len(response),
                "has_tool_calls": has_tool_calls,
                "has_final_answer": has_final_answer,
                "message": t('report.llmResponse', hasToolCalls=has_tool_calls, hasFinalAnswer=has_final_answer)
            }
        )
    
    def log_section_content(
        self,
        section_title: str,
        section_index: int,
        content: str,
        tool_calls_count: int
    ):
        """记录章节内容生成完成（仅记录内容，不代表整个章节完成）"""
        self.log(
            action="section_content",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "content": content,  # 完整内容，不截断
                "content_length": len(content),
                "tool_calls_count": tool_calls_count,
                "message": t('report.sectionContentDone', title=section_title)
            }
        )
    
    def log_section_full_complete(
        self,
        section_title: str,
        section_index: int,
        full_content: str
    ):
        """
        记录章节生成完成

        前端应监听此日志来判断一个章节是否真正完成，并获取完整内容
        """
        self.log(
            action="section_complete",
            stage="generating",
            section_title=section_title,
            section_index=section_index,
            details={
                "content": full_content,
                "content_length": len(full_content),
                "message": t('report.sectionComplete', title=section_title)
            }
        )
    
    def log_report_complete(self, total_sections: int, total_time_seconds: float):
        """记录报告生成完成"""
        self.log(
            action="report_complete",
            stage="completed",
            details={
                "total_sections": total_sections,
                "total_time_seconds": round(total_time_seconds, 2),
                "message": t('report.reportComplete')
            }
        )
    
    def log_error(self, error_message: str, stage: str, section_title: str = None):
        """记录错误"""
        self.log(
            action="error",
            stage=stage,
            section_title=section_title,
            section_index=None,
            details={
                "error": error_message,
                "message": t('report.errorOccurred', error=error_message)
            }
        )


class ReportConsoleLogger:
    """
    Report Agent 控制台日志记录器
    
    将控制台风格的日志（INFO、WARNING等）写入报告文件夹中的 console_log.txt 文件。
    这些日志与 agent_log.jsonl 不同，是纯文本格式的控制台输出。
    """
    
    def __init__(self, report_id: str):
        """
        初始化控制台日志记录器
        
        Args:
            report_id: 报告ID，用于确定日志文件路径
        """
        self.report_id = report_id
        self.log_file_path = os.path.join(
            Config.UPLOAD_FOLDER, 'reports', report_id, 'console_log.txt'
        )
        self._ensure_log_file()
        self._file_handler = None
        self._setup_file_handler()
    
    def _ensure_log_file(self):
        """确保日志文件所在目录存在"""
        log_dir = os.path.dirname(self.log_file_path)
        os.makedirs(log_dir, exist_ok=True)
    
    def _setup_file_handler(self):
        """设置文件处理器，将日志同时写入文件"""
        import logging
        
        # 创建文件处理器
        self._file_handler = logging.FileHandler(
            self.log_file_path,
            mode='a',
            encoding='utf-8'
        )
        self._file_handler.setLevel(logging.INFO)
        
        # 使用与控制台相同的简洁格式
        formatter = logging.Formatter(
            '[%(asctime)s] %(levelname)s: %(message)s',
            datefmt='%H:%M:%S'
        )
        self._file_handler.setFormatter(formatter)
        
        # 添加到 report_agent 相关的 logger
        loggers_to_attach = [
            'mirofish.report_agent',
            'mirofish.zep_tools',
        ]
        
        for logger_name in loggers_to_attach:
            target_logger = logging.getLogger(logger_name)
            # 避免重复添加
            if self._file_handler not in target_logger.handlers:
                target_logger.addHandler(self._file_handler)
    
    def close(self):
        """关闭文件处理器并从 logger 中移除"""
        import logging
        
        if self._file_handler:
            loggers_to_detach = [
                'mirofish.report_agent',
                'mirofish.zep_tools',
            ]
            
            for logger_name in loggers_to_detach:
                target_logger = logging.getLogger(logger_name)
                if self._file_handler in target_logger.handlers:
                    target_logger.removeHandler(self._file_handler)
            
            self._file_handler.close()
            self._file_handler = None
    
    def __del__(self):
        """析构时确保关闭文件处理器"""
        self.close()


class ReportStatus(str, Enum):
    """报告状态"""
    PENDING = "pending"
    PLANNING = "planning"
    GENERATING = "generating"
    COMPLETED = "completed"
    FAILED = "failed"


@dataclass
class ReportSection:
    """报告章节"""
    title: str
    content: str = ""

    def to_dict(self) -> Dict[str, Any]:
        return {
            "title": self.title,
            "content": self.content
        }

    def to_markdown(self, level: int = 2) -> str:
        """转换为Markdown格式"""
        md = f"{'#' * level} {self.title}\n\n"
        if self.content:
            md += f"{self.content}\n\n"
        return md


@dataclass
class ReportOutline:
    """报告大纲"""
    title: str
    summary: str
    sections: List[ReportSection]
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "title": self.title,
            "summary": self.summary,
            "sections": [s.to_dict() for s in self.sections]
        }
    
    def to_markdown(self) -> str:
        """转换为Markdown格式"""
        md = f"# {self.title}\n\n"
        md += f"> {self.summary}\n\n"
        for section in self.sections:
            md += section.to_markdown()
        return md


@dataclass
class Report:
    """完整报告"""
    report_id: str
    simulation_id: str
    graph_id: str
    simulation_requirement: str
    status: ReportStatus
    outline: Optional[ReportOutline] = None
    markdown_content: str = ""
    created_at: str = ""
    completed_at: str = ""
    error: Optional[str] = None
    
    def to_dict(self) -> Dict[str, Any]:
        return {
            "report_id": self.report_id,
            "simulation_id": self.simulation_id,
            "graph_id": self.graph_id,
            "simulation_requirement": self.simulation_requirement,
            "status": self.status.value,
            "outline": self.outline.to_dict() if self.outline else None,
            "markdown_content": self.markdown_content,
            "created_at": self.created_at,
            "completed_at": self.completed_at,
            "error": self.error
        }


# ═══════════════════════════════════════════════════════════════
# Prompt 模板常量
# ═══════════════════════════════════════════════════════════════

# ── 工具描述 ──

TOOL_DESC_INSIGHT_FORGE = """\
[Deep insight search]
Use this tool for important report sections and strategic analysis. It decomposes a question into several focused sub-questions, searches simulation facts, entities, and relationship chains, then returns evidence-rich material for the report.

Best use cases:
- Core drivers, tensions, causal mechanisms, winners and losers
- Lead scenario, weak signals, contradictions, decision points
- Any section that needs more than a simple lookup

Input:
- query: the question or topic to analyze
- report_context: optional context from the current report section

Output:
- generated sub-questions
- simulation facts suitable for citation
- relevant entities and relationship chains
"""

TOOL_DESC_PANORAMA_SEARCH = """\
[Panorama search]
Use this tool to understand the full simulation picture and event development. It retrieves current and historical facts, including expired or superseded facts.

Best use cases:
- What happened over time
- Which actors appeared in the simulation
- How the discussion, positions, or conflicts evolved

Output:
- active facts
- historical facts
- involved entities
- basic counts
"""

TOOL_DESC_QUICK_SEARCH = """\
[Quick search]
Use this lightweight tool for simple, direct lookups. It is useful for checking one fact, one actor, one term, or a narrow topic.

Input:
- query: a precise search phrase

Output:
- matching facts from the simulation memory
"""

TOOL_DESC_INTERVIEW_AGENTS = """\
[Agent interviews]
Use this tool to interview simulated agents and add direct perspectives to the report. The tool selects relevant agents, generates interview questions if needed, calls the simulation interview API on both Twitter and Reddit contexts, and summarizes the responses.

Best use cases:
- Need direct voice from simulated actors
- Need contrasting perspectives
- Need report sections with interview excerpts or stakeholder views

Input:
- interview_topic: interview topic or requirement
- max_agents: optional number of agents to interview, default 5, max 10

Output:
- selected agents and rationale
- interview questions
- raw answers from the simulated platforms
- interview summary and key viewpoints
"""

# ── 大纲规划 prompt ──

PLAN_SYSTEM_PROMPT = """\
You are an expert writer of future-prediction management reports. You observe a simulated world from a full-system perspective: agent profiles, actions, statements, interactions, and graph facts.

Core idea: the simulation is a rehearsal of a possible future under the supplied conditions. The report should explain what happened in that future rehearsal, how actor groups reacted, and which strategic patterns, risks, and opportunities emerged.

Your task:
1. Create a concise report outline.
2. Focus on predicted dynamics, actor reactions, emerging conflicts, alliances, market shifts, risks, and options.
3. Keep the report management-oriented and useful for decisions.

Rules:
- Produce 2 to 5 sections.
- Each section should be a substantial report section, not a subheading placeholder.
- Return valid JSON only.
- Use the requested response language for title, summary, section titles, and descriptions.

JSON schema:
{
  "title": "Report title",
  "summary": "One-sentence core prediction",
  "sections": [
    {"title": "Section title", "description": "What this section covers"}
  ]
}
"""

PLAN_USER_PROMPT_TEMPLATE = """\
Simulation requirement:
{simulation_requirement}

Simulation world size:
- Total graph nodes: {total_nodes}
- Total graph edges: {total_edges}
- Entity type distribution: {entity_types}
- Active agents/entities: {total_entities}

Sample facts from the simulated future:
{related_facts_json}

Design the best report structure for this future rehearsal. Focus on the most decision-relevant findings. Return JSON only.
"""

SECTION_SYSTEM_PROMPT_TEMPLATE = """\
You are writing one section of a future-prediction management report.

Report title: {report_title}
Report summary: {report_summary}
Simulation requirement: {simulation_requirement}
Current section: {section_title}

The simulated world is evidence. Agent behavior, posts, comments, interviews, and graph facts represent the predicted future under the scenario conditions.

Rules:
1. Use tools to inspect the simulation before writing. Each section should use at least 3 tool calls and at most 5.
2. Base claims on tool results. Do not invent facts.
3. Translate any quoted or retrieved material into the requested report language before including it.
4. Write the entire section in the requested report language.
5. Do not use Markdown headings inside the section. The system adds section titles. Use paragraphs, bullets, bold emphasis, and quote blocks only.
6. If evidence is thin, state that clearly and explain the implication.

Available tools:
{tools_description}

Use a ReACT loop. In each response do exactly one of these:

A) Call one tool:
<tool_call>
{{"name": "tool_name", "parameters": {{"parameter": "value"}}}}
</tool_call>

B) Write the final section content:
Final Answer: ...

Never include a tool call and Final Answer in the same response. Never fabricate tool output.
"""

SECTION_USER_PROMPT_TEMPLATE = """\
Previously completed sections, for continuity and to avoid repetition:
{previous_content}

Current task: write the section "{section_title}".

Start by thinking about which simulation evidence is needed, then call a tool. After enough evidence has been gathered, output Final Answer with section body text only. Do not repeat the section title. Do not use Markdown headings.
"""

REACT_OBSERVATION_TEMPLATE = """\
Observation from tool {tool_name}:
{result}

Tool calls used: {tool_calls_count}/{max_tool_calls}. Used tools: {used_tools_str}. {unused_hint}
If the evidence is sufficient, respond with Final Answer and write the section. If more evidence is needed, call one additional tool.
"""

REACT_INSUFFICIENT_TOOLS_MSG = (
    "You have used {tool_calls_count} tool calls. At least {min_tool_calls} are required before Final Answer. "
    "Call another tool to inspect more simulation evidence. {unused_hint}"
)

REACT_INSUFFICIENT_TOOLS_MSG_ALT = (
    "You have only used {tool_calls_count} tool calls. At least {min_tool_calls} are required. "
    "Call a tool now. {unused_hint}"
)

REACT_TOOL_LIMIT_MSG = (
    "The tool-call limit has been reached ({tool_calls_count}/{max_tool_calls}). "
    "Now write the section using Final Answer."
)

REACT_UNUSED_TOOLS_HINT = "Unused tools worth considering: {unused_list}."

REACT_FORCE_FINAL_MSG = "Tool-call or iteration limit reached. Write Final Answer now."

CHAT_SYSTEM_PROMPT_TEMPLATE = """\
You are a concise simulation-analysis assistant.

Simulation requirement: {simulation_requirement}

Existing report:
{report_content}

Rules:
1. Prefer the existing report when it answers the question.
2. Answer directly and concisely.
3. Use tools only if the report is insufficient.
4. Use the requested response language for the entire answer.

Available tools:
{tools_description}

Tool-call format:
<tool_call>
{{"name": "tool_name", "parameters": {{"parameter": "value"}}}}
</tool_call>
"""

CHAT_OBSERVATION_SUFFIX = "\n\nAnswer the user concisely in the requested response language."

# ═══════════════════════════════════════════════════════════════
# ReportAgent 主类
# ═══════════════════════════════════════════════════════════════


class ReportAgent:
    """
    Report Agent - 模拟报告生成Agent

    采用ReACT（Reasoning + Acting）模式：
    1. 规划阶段：分析模拟需求，规划报告目录结构
    2. 生成阶段：逐章节生成内容，每章节可多次调用工具获取信息
    3. 反思阶段：检查内容完整性和准确性
    """
    
    # 最大工具调用次数（每个章节）
    MAX_TOOL_CALLS_PER_SECTION = 5
    
    # 最大反思轮数
    MAX_REFLECTION_ROUNDS = 3
    
    # 对话中的最大工具调用次数
    MAX_TOOL_CALLS_PER_CHAT = 2
    
    def __init__(
        self, 
        graph_id: str,
        simulation_id: str,
        simulation_requirement: str,
        llm_client: Optional[LLMClient] = None,
        zep_tools: Optional[ZepToolsService] = None
    ):
        """
        初始化Report Agent
        
        Args:
            graph_id: 图谱ID
            simulation_id: 模拟ID
            simulation_requirement: 模拟需求描述
            llm_client: LLM客户端（可选）
            zep_tools: Zep工具服务（可选）
        """
        self.graph_id = graph_id
        self.simulation_id = simulation_id
        self.simulation_requirement = simulation_requirement
        
        self.llm = llm_client or LLMClient()
        self.zep_tools = zep_tools or ZepToolsService()
        
        # 工具定义
        self.tools = self._define_tools()
        
        # 日志记录器（在 generate_report 中初始化）
        self.report_logger: Optional[ReportLogger] = None
        # 控制台日志记录器（在 generate_report 中初始化）
        self.console_logger: Optional[ReportConsoleLogger] = None
        
        logger.info(t('report.agentInitDone', graphId=graph_id, simulationId=simulation_id))
    
    def _define_tools(self) -> Dict[str, Dict[str, Any]]:
        """定义可用工具"""
        return {
            "insight_forge": {
                "name": "insight_forge",
                "description": TOOL_DESC_INSIGHT_FORGE,
                "parameters": {
                    "query": "Question or topic to analyze deeply",
                    "report_context": "Optional current section context for better sub-questions"
                }
            },
            "panorama_search": {
                "name": "panorama_search",
                "description": TOOL_DESC_PANORAMA_SEARCH,
                "parameters": {
                    "query": "搜索查询，用于相关性排序",
                    "include_expired": "是否包含过期/历史内容（默认True）"
                }
            },
            "quick_search": {
                "name": "quick_search",
                "description": TOOL_DESC_QUICK_SEARCH,
                "parameters": {
                    "query": "搜索查询字符串",
                    "limit": "返回结果数量（可选，默认10）"
                }
            },
            "interview_agents": {
                "name": "interview_agents",
                "description": TOOL_DESC_INTERVIEW_AGENTS,
                "parameters": {
                    "interview_topic": "Interview topic or requirement",
                    "max_agents": "Maximum number of agents to interview, default 5, max 10"
                }
            }
        }
    
    def _execute_tool(self, tool_name: str, parameters: Dict[str, Any], report_context: str = "") -> str:
        """
        执行工具调用
        
        Args:
            tool_name: 工具名称
            parameters: 工具参数
            report_context: 报告上下文（用于InsightForge）
            
        Returns:
            工具执行结果（文本格式）
        """
        logger.info(t('report.executingTool', toolName=tool_name, params=parameters))
        
        try:
            if tool_name == "insight_forge":
                query = parameters.get("query", "")
                ctx = parameters.get("report_context", "") or report_context
                result = self.zep_tools.insight_forge(
                    graph_id=self.graph_id,
                    query=query,
                    simulation_requirement=self.simulation_requirement,
                    report_context=ctx
                )
                return result.to_text()
            
            elif tool_name == "panorama_search":
                # 广度搜索 - 获取全貌
                query = parameters.get("query", "")
                include_expired = parameters.get("include_expired", True)
                if isinstance(include_expired, str):
                    include_expired = include_expired.lower() in ['true', '1', 'yes']
                result = self.zep_tools.panorama_search(
                    graph_id=self.graph_id,
                    query=query,
                    include_expired=include_expired
                )
                return result.to_text()
            
            elif tool_name == "quick_search":
                # 简单搜索 - 快速检索
                query = parameters.get("query", "")
                limit = parameters.get("limit", 10)
                if isinstance(limit, str):
                    limit = int(limit)
                result = self.zep_tools.quick_search(
                    graph_id=self.graph_id,
                    query=query,
                    limit=limit
                )
                return result.to_text()
            
            elif tool_name == "interview_agents":
                # 深度采访 - 调用真实的OASIS采访API获取模拟Agent的回答（双平台）
                interview_topic = parameters.get("interview_topic", parameters.get("query", ""))
                max_agents = parameters.get("max_agents", 5)
                if isinstance(max_agents, str):
                    max_agents = int(max_agents)
                max_agents = min(max_agents, 10)
                result = self.zep_tools.interview_agents(
                    simulation_id=self.simulation_id,
                    interview_requirement=interview_topic,
                    simulation_requirement=self.simulation_requirement,
                    max_agents=max_agents
                )
                return result.to_text()
            
            # ========== 向后兼容的旧工具（内部重定向到新工具） ==========
            
            elif tool_name == "search_graph":
                # 重定向到 quick_search
                logger.info(t('report.redirectToQuickSearch'))
                return self._execute_tool("quick_search", parameters, report_context)
            
            elif tool_name == "get_graph_statistics":
                result = self.zep_tools.get_graph_statistics(self.graph_id)
                return json.dumps(result, ensure_ascii=False, indent=2)
            
            elif tool_name == "get_entity_summary":
                entity_name = parameters.get("entity_name", "")
                result = self.zep_tools.get_entity_summary(
                    graph_id=self.graph_id,
                    entity_name=entity_name
                )
                return json.dumps(result, ensure_ascii=False, indent=2)
            
            elif tool_name == "get_simulation_context":
                # 重定向到 insight_forge，因为它更强大
                logger.info(t('report.redirectToInsightForge'))
                query = parameters.get("query", self.simulation_requirement)
                return self._execute_tool("insight_forge", {"query": query}, report_context)
            
            elif tool_name == "get_entities_by_type":
                entity_type = parameters.get("entity_type", "")
                nodes = self.zep_tools.get_entities_by_type(
                    graph_id=self.graph_id,
                    entity_type=entity_type
                )
                result = [n.to_dict() for n in nodes]
                return json.dumps(result, ensure_ascii=False, indent=2)
            
            else:
                return f"未知工具: {tool_name}。请使用以下工具之一: insight_forge, panorama_search, quick_search"
                
        except Exception as e:
            logger.error(t('report.toolExecFailed', toolName=tool_name, error=str(e)))
            return f"工具执行失败: {str(e)}"
    
    # 合法的工具名称集合，用于裸 JSON 兜底解析时校验
    VALID_TOOL_NAMES = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}

    def _parse_tool_calls(self, response: str) -> List[Dict[str, Any]]:
        """
        从LLM响应中解析工具调用

        支持的格式（按优先级）：
        1. <tool_call>{"name": "tool_name", "parameters": {...}}</tool_call>
        2. 裸 JSON（响应整体或单行就是一个工具调用 JSON）
        """
        tool_calls = []

        # 格式1: XML风格（标准格式）
        xml_pattern = r'<tool_call>\s*(\{.*?\})\s*</tool_call>'
        for match in re.finditer(xml_pattern, response, re.DOTALL):
            try:
                call_data = json.loads(match.group(1))
                tool_calls.append(call_data)
            except json.JSONDecodeError:
                pass

        if tool_calls:
            return tool_calls

        # 格式2: 兜底 - LLM 直接输出裸 JSON（没包 <tool_call> 标签）
        # 只在格式1未匹配时尝试，避免误匹配正文中的 JSON
        stripped = response.strip()
        if stripped.startswith('{') and stripped.endswith('}'):
            try:
                call_data = json.loads(stripped)
                if self._is_valid_tool_call(call_data):
                    tool_calls.append(call_data)
                    return tool_calls
            except json.JSONDecodeError:
                pass

        # 响应可能包含思考文字 + 裸 JSON，尝试提取最后一个 JSON 对象
        json_pattern = r'(\{"(?:name|tool)"\s*:.*?\})\s*$'
        match = re.search(json_pattern, stripped, re.DOTALL)
        if match:
            try:
                call_data = json.loads(match.group(1))
                if self._is_valid_tool_call(call_data):
                    tool_calls.append(call_data)
            except json.JSONDecodeError:
                pass

        return tool_calls

    def _is_valid_tool_call(self, data: dict) -> bool:
        """校验解析出的 JSON 是否是合法的工具调用"""
        # 支持 {"name": ..., "parameters": ...} 和 {"tool": ..., "params": ...} 两种键名
        tool_name = data.get("name") or data.get("tool")
        if tool_name and tool_name in self.VALID_TOOL_NAMES:
            # 统一键名为 name / parameters
            if "tool" in data:
                data["name"] = data.pop("tool")
            if "params" in data and "parameters" not in data:
                data["parameters"] = data.pop("params")
            return True
        return False
    
    def _get_tools_description(self) -> str:
        """生成工具描述文本"""
        desc_parts = ["Available tools:"]
        for name, tool in self.tools.items():
            params_desc = ", ".join([f"{k}: {v}" for k, v in tool["parameters"].items()])
            desc_parts.append(f"- {name}: {tool['description']}")
            if params_desc:
                desc_parts.append(f"  参数: {params_desc}")
        return "\n".join(desc_parts)
    
    def plan_outline(
        self, 
        progress_callback: Optional[Callable] = None
    ) -> ReportOutline:
        """
        规划报告大纲
        
        使用LLM分析模拟需求，规划报告的目录结构
        
        Args:
            progress_callback: 进度回调函数
            
        Returns:
            ReportOutline: 报告大纲
        """
        logger.info(t('report.startPlanningOutline'))
        
        if progress_callback:
            progress_callback("planning", 0, t('progress.analyzingRequirements'))
        
        # 首先获取模拟上下文
        context = self.zep_tools.get_simulation_context(
            graph_id=self.graph_id,
            simulation_requirement=self.simulation_requirement
        )
        
        if progress_callback:
            progress_callback("planning", 30, t('progress.generatingOutline'))
        
        system_prompt = f"{PLAN_SYSTEM_PROMPT}\n\n{get_language_instruction()}"
        user_prompt = PLAN_USER_PROMPT_TEMPLATE.format(
            simulation_requirement=self.simulation_requirement,
            total_nodes=context.get('graph_statistics', {}).get('total_nodes', 0),
            total_edges=context.get('graph_statistics', {}).get('total_edges', 0),
            entity_types=list(context.get('graph_statistics', {}).get('entity_types', {}).keys()),
            total_entities=context.get('total_entities', 0),
            related_facts_json=json.dumps(context.get('related_facts', [])[:10], ensure_ascii=False, indent=2),
        )

        try:
            response = self.llm.chat_json(
                messages=[
                    {"role": "system", "content": system_prompt},
                    {"role": "user", "content": user_prompt}
                ],
                temperature=0.3
            )
            
            if progress_callback:
                progress_callback("planning", 80, t('progress.parsingOutline'))
            
            # 解析大纲
            sections = []
            for section_data in response.get("sections", []):
                sections.append(ReportSection(
                    title=section_data.get("title", ""),
                    content=""
                ))
            
            outline = ReportOutline(
                title=response.get("title", "Simulation Analysis Report"),
                summary=response.get("summary", ""),
                sections=sections
            )
            
            if progress_callback:
                progress_callback("planning", 100, t('progress.outlinePlanComplete'))
            
            logger.info(t('report.outlinePlanDone', count=len(sections)))
            return outline
            
        except Exception as e:
            logger.error(t('report.outlinePlanFailed', error=str(e)))
            # 返回默认大纲（3个章节，作为fallback）
            return ReportOutline(
                title="Future Prediction Report",
                summary="Future trends and risk analysis based on simulation evidence",
                sections=[
                    ReportSection(title="Scenario and Key Findings"),
                    ReportSection(title="Actor Behavior Analysis"),
                    ReportSection(title="Trends and Risks")
                ]
            )
    
    def _generate_section_react(
        self, 
        section: ReportSection,
        outline: ReportOutline,
        previous_sections: List[str],
        progress_callback: Optional[Callable] = None,
        section_index: int = 0
    ) -> str:
        """
        使用ReACT模式生成单个章节内容
        
        ReACT循环：
        1. Thought（思考）- 分析需要什么信息
        2. Action（行动）- 调用工具获取信息
        3. Observation（观察）- 分析工具返回结果
        4. 重复直到信息足够或达到最大次数
        5. Final Answer（最终回答）- 生成章节内容
        
        Args:
            section: 要生成的章节
            outline: 完整大纲
            previous_sections: 之前章节的内容（用于保持连贯性）
            progress_callback: 进度回调
            section_index: 章节索引（用于日志记录）
            
        Returns:
            章节内容（Markdown格式）
        """
        logger.info(t('report.reactGenerateSection', title=section.title))
        
        # 记录章节开始日志
        if self.report_logger:
            self.report_logger.log_section_start(section.title, section_index)
        
        system_prompt = SECTION_SYSTEM_PROMPT_TEMPLATE.format(
            report_title=outline.title,
            report_summary=outline.summary,
            simulation_requirement=self.simulation_requirement,
            section_title=section.title,
            tools_description=self._get_tools_description(),
        )
        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"

        # 构建用户prompt - 每个已完成章节各传入最大4000字
        if previous_sections:
            previous_parts = []
            for sec in previous_sections:
                # 每个章节最多4000字
                truncated = sec[:4000] + "..." if len(sec) > 4000 else sec
                previous_parts.append(truncated)
            previous_content = "\n\n---\n\n".join(previous_parts)
        else:
            previous_content = "（这是第一个章节）"
        
        user_prompt = SECTION_USER_PROMPT_TEMPLATE.format(
            previous_content=previous_content,
            section_title=section.title,
        )

        messages = [
            {"role": "system", "content": system_prompt},
            {"role": "user", "content": user_prompt}
        ]
        
        # ReACT循环
        tool_calls_count = 0
        max_iterations = 5  # 最大迭代轮数
        min_tool_calls = 3  # 最少工具调用次数
        conflict_retries = 0  # 工具调用与Final Answer同时出现的连续冲突次数
        used_tools = set()  # 记录已调用过的工具名
        all_tools = {"insight_forge", "panorama_search", "quick_search", "interview_agents"}

        # 报告上下文，用于InsightForge的子问题生成
        report_context = f"Section title: {section.title}\nSimulation requirement: {self.simulation_requirement}"
        
        for iteration in range(max_iterations):
            if progress_callback:
                progress_callback(
                    "generating", 
                    int((iteration / max_iterations) * 100),
                    t('progress.deepSearchAndWrite', current=tool_calls_count, max=self.MAX_TOOL_CALLS_PER_SECTION)
                )
            
            # 调用LLM
            response = self.llm.chat(
                messages=messages,
                temperature=0.5,
                max_tokens=4096
            )

            # 检查 LLM 返回是否为 None（API 异常或内容为空）
            if response is None:
                logger.warning(t('report.sectionIterNone', title=section.title, iteration=iteration + 1))
                # 如果还有迭代次数，添加消息并重试
                if iteration < max_iterations - 1:
                    messages.append({"role": "assistant", "content": "(empty response)"})
                    messages.append({"role": "user", "content": "Please continue in the requested response language."})
                    continue
                # 最后一次迭代也返回 None，跳出循环进入强制收尾
                break

            logger.debug(f"LLM响应: {response[:200]}...")

            # 解析一次，复用结果
            tool_calls = self._parse_tool_calls(response)
            has_tool_calls = bool(tool_calls)
            has_final_answer = "Final Answer:" in response

            # ── 冲突处理：LLM 同时输出了工具调用和 Final Answer ──
            if has_tool_calls and has_final_answer:
                conflict_retries += 1
                logger.warning(
                    t('report.sectionConflict', title=section.title, iteration=iteration+1, conflictCount=conflict_retries)
                )

                if conflict_retries <= 2:
                    # 前两次：丢弃本次响应，要求 LLM 重新回复
                    messages.append({"role": "assistant", "content": response})
                    messages.append({
                        "role": "user",
                        "content": (
                            "Format error: your response contains both a tool call and Final Answer.\n"
                            "Each response must do exactly one of these two things:\n"
                            "- Call one tool by outputting one <tool_call> block.\n"
                            "- Output final content starting with 'Final Answer:' and no <tool_call>.\n"
                            "Reply again and do only one of the two."
                        ),
                    })
                    continue
                else:
                    # 第三次：降级处理，截断到第一个工具调用，强制执行
                    logger.warning(
                        t('report.sectionConflictDowngrade', title=section.title, conflictCount=conflict_retries)
                    )
                    first_tool_end = response.find('</tool_call>')
                    if first_tool_end != -1:
                        response = response[:first_tool_end + len('</tool_call>')]
                        tool_calls = self._parse_tool_calls(response)
                        has_tool_calls = bool(tool_calls)
                    has_final_answer = False
                    conflict_retries = 0

            # 记录 LLM 响应日志
            if self.report_logger:
                self.report_logger.log_llm_response(
                    section_title=section.title,
                    section_index=section_index,
                    response=response,
                    iteration=iteration + 1,
                    has_tool_calls=has_tool_calls,
                    has_final_answer=has_final_answer
                )

            # ── 情况1：LLM 输出了 Final Answer ──
            if has_final_answer:
                # 工具调用次数不足，拒绝并要求继续调工具
                if tool_calls_count < min_tool_calls:
                    messages.append({"role": "assistant", "content": response})
                    unused_tools = all_tools - used_tools
                    unused_hint = f"(Unused tools worth considering: {', '.join(unused_tools)})" if unused_tools else ""
                    messages.append({
                        "role": "user",
                        "content": REACT_INSUFFICIENT_TOOLS_MSG.format(
                            tool_calls_count=tool_calls_count,
                            min_tool_calls=min_tool_calls,
                            unused_hint=unused_hint,
                        ),
                    })
                    continue

                # 正常结束
                final_answer = response.split("Final Answer:")[-1].strip()
                logger.info(t('report.sectionGenDone', title=section.title, count=tool_calls_count))

                if self.report_logger:
                    self.report_logger.log_section_content(
                        section_title=section.title,
                        section_index=section_index,
                        content=final_answer,
                        tool_calls_count=tool_calls_count
                    )
                return final_answer

            # ── 情况2：LLM 尝试调用工具 ──
            if has_tool_calls:
                # 工具额度已耗尽 → 明确告知，要求输出 Final Answer
                if tool_calls_count >= self.MAX_TOOL_CALLS_PER_SECTION:
                    messages.append({"role": "assistant", "content": response})
                    messages.append({
                        "role": "user",
                        "content": REACT_TOOL_LIMIT_MSG.format(
                            tool_calls_count=tool_calls_count,
                            max_tool_calls=self.MAX_TOOL_CALLS_PER_SECTION,
                        ),
                    })
                    continue

                # 只执行第一个工具调用
                call = tool_calls[0]
                if len(tool_calls) > 1:
                    logger.info(t('report.multiToolOnlyFirst', total=len(tool_calls), toolName=call['name']))

                if self.report_logger:
                    self.report_logger.log_tool_call(
                        section_title=section.title,
                        section_index=section_index,
                        tool_name=call["name"],
                        parameters=call.get("parameters", {}),
                        iteration=iteration + 1
                    )

                result = self._execute_tool(
                    call["name"],
                    call.get("parameters", {}),
                    report_context=report_context
                )

                if self.report_logger:
                    self.report_logger.log_tool_result(
                        section_title=section.title,
                        section_index=section_index,
                        tool_name=call["name"],
                        result=result,
                        iteration=iteration + 1
                    )

                tool_calls_count += 1
                used_tools.add(call['name'])

                # 构建未使用工具提示
                unused_tools = all_tools - used_tools
                unused_hint = ""
                if unused_tools and tool_calls_count < self.MAX_TOOL_CALLS_PER_SECTION:
                    unused_hint = REACT_UNUSED_TOOLS_HINT.format(unused_list=", ".join(unused_tools))

                messages.append({"role": "assistant", "content": response})
                messages.append({
                    "role": "user",
                    "content": REACT_OBSERVATION_TEMPLATE.format(
                        tool_name=call["name"],
                        result=result,
                        tool_calls_count=tool_calls_count,
                        max_tool_calls=self.MAX_TOOL_CALLS_PER_SECTION,
                        used_tools_str=", ".join(used_tools),
                        unused_hint=unused_hint,
                    ),
                })
                continue

            # ── 情况3：既没有工具调用，也没有 Final Answer ──
            messages.append({"role": "assistant", "content": response})

            if tool_calls_count < min_tool_calls:
                # 工具调用次数不足，推荐未用过的工具
                unused_tools = all_tools - used_tools
                unused_hint = f"(Unused tools worth considering: {', '.join(unused_tools)})" if unused_tools else ""

                messages.append({
                    "role": "user",
                    "content": REACT_INSUFFICIENT_TOOLS_MSG_ALT.format(
                        tool_calls_count=tool_calls_count,
                        min_tool_calls=min_tool_calls,
                        unused_hint=unused_hint,
                    ),
                })
                continue

            # 工具调用已足够，LLM 输出了内容但没带 "Final Answer:" 前缀
            # 直接将这段内容作为最终答案，不再空转
            logger.info(t('report.sectionNoPrefix', title=section.title, count=tool_calls_count))
            final_answer = response.strip()

            if self.report_logger:
                self.report_logger.log_section_content(
                    section_title=section.title,
                    section_index=section_index,
                    content=final_answer,
                    tool_calls_count=tool_calls_count
                )
            return final_answer
        
        # 达到最大迭代次数，强制生成内容
        logger.warning(t('report.sectionMaxIter', title=section.title))
        messages.append({"role": "user", "content": REACT_FORCE_FINAL_MSG})
        
        response = self.llm.chat(
            messages=messages,
            temperature=0.5,
            max_tokens=4096
        )

        # 检查强制收尾时 LLM 返回是否为 None
        if response is None:
            logger.error(t('report.sectionForceFailed', title=section.title))
            final_answer = t('report.sectionGenFailedContent')
        elif "Final Answer:" in response:
            final_answer = response.split("Final Answer:")[-1].strip()
        else:
            final_answer = response
        
        # 记录章节内容生成完成日志
        if self.report_logger:
            self.report_logger.log_section_content(
                section_title=section.title,
                section_index=section_index,
                content=final_answer,
                tool_calls_count=tool_calls_count
            )
        
        return final_answer
    
    def generate_report(
        self, 
        progress_callback: Optional[Callable[[str, int, str], None]] = None,
        report_id: Optional[str] = None
    ) -> Report:
        """
        生成完整报告（分章节实时输出）
        
        每个章节生成完成后立即保存到文件夹，不需要等待整个报告完成。
        文件结构：
        reports/{report_id}/
            meta.json       - 报告元信息
            outline.json    - 报告大纲
            progress.json   - 生成进度
            section_01.md   - 第1章节
            section_02.md   - 第2章节
            ...
            full_report.md  - 完整报告
        
        Args:
            progress_callback: 进度回调函数 (stage, progress, message)
            report_id: 报告ID（可选，如果不传则自动生成）
            
        Returns:
            Report: 完整报告
        """
        import uuid
        
        # 如果没有传入 report_id，则自动生成
        if not report_id:
            report_id = f"report_{uuid.uuid4().hex[:12]}"
        start_time = datetime.now()
        
        report = Report(
            report_id=report_id,
            simulation_id=self.simulation_id,
            graph_id=self.graph_id,
            simulation_requirement=self.simulation_requirement,
            status=ReportStatus.PENDING,
            created_at=datetime.now().isoformat()
        )
        
        # 已完成的章节标题列表（用于进度追踪）
        completed_section_titles = []
        
        try:
            # 初始化：创建报告文件夹并保存初始状态
            ReportManager._ensure_report_folder(report_id)
            
            # 初始化日志记录器（结构化日志 agent_log.jsonl）
            self.report_logger = ReportLogger(report_id)
            self.report_logger.log_start(
                simulation_id=self.simulation_id,
                graph_id=self.graph_id,
                simulation_requirement=self.simulation_requirement
            )
            
            # 初始化控制台日志记录器（console_log.txt）
            self.console_logger = ReportConsoleLogger(report_id)
            
            ReportManager.update_progress(
                report_id, "pending", 0, t('progress.initReport'),
                completed_sections=[]
            )
            ReportManager.save_report(report)
            
            # 阶段1: 规划大纲
            report.status = ReportStatus.PLANNING
            ReportManager.update_progress(
                report_id, "planning", 5, t('progress.startPlanningOutline'),
                completed_sections=[]
            )
            
            # 记录规划开始日志
            self.report_logger.log_planning_start()
            
            if progress_callback:
                progress_callback("planning", 0, t('progress.startPlanningOutline'))
            
            outline = self.plan_outline(
                progress_callback=lambda stage, prog, msg: 
                    progress_callback(stage, prog // 5, msg) if progress_callback else None
            )
            report.outline = outline
            
            # 记录规划完成日志
            self.report_logger.log_planning_complete(outline.to_dict())
            
            # 保存大纲到文件
            ReportManager.save_outline(report_id, outline)
            ReportManager.update_progress(
                report_id, "planning", 15, t('progress.outlineDone', count=len(outline.sections)),
                completed_sections=[]
            )
            ReportManager.save_report(report)
            
            logger.info(t('report.outlineSavedToFile', reportId=report_id))
            
            # 阶段2: 逐章节生成（分章节保存）
            report.status = ReportStatus.GENERATING
            
            total_sections = len(outline.sections)
            generated_sections = []  # 保存内容用于上下文
            
            for i, section in enumerate(outline.sections):
                section_num = i + 1
                base_progress = 20 + int((i / total_sections) * 70)
                
                # 更新进度
                ReportManager.update_progress(
                    report_id, "generating", base_progress,
                    t('progress.generatingSection', title=section.title, current=section_num, total=total_sections),
                    current_section=section.title,
                    completed_sections=completed_section_titles
                )

                if progress_callback:
                    progress_callback(
                        "generating",
                        base_progress,
                        t('progress.generatingSection', title=section.title, current=section_num, total=total_sections)
                    )
                
                # 生成主章节内容
                section_content = self._generate_section_react(
                    section=section,
                    outline=outline,
                    previous_sections=generated_sections,
                    progress_callback=lambda stage, prog, msg:
                        progress_callback(
                            stage, 
                            base_progress + int(prog * 0.7 / total_sections),
                            msg
                        ) if progress_callback else None,
                    section_index=section_num
                )
                
                section.content = section_content
                generated_sections.append(f"## {section.title}\n\n{section_content}")

                # 保存章节
                ReportManager.save_section(report_id, section_num, section)
                completed_section_titles.append(section.title)

                # 记录章节完成日志
                full_section_content = f"## {section.title}\n\n{section_content}"

                if self.report_logger:
                    self.report_logger.log_section_full_complete(
                        section_title=section.title,
                        section_index=section_num,
                        full_content=full_section_content.strip()
                    )

                logger.info(t('report.sectionSaved', reportId=report_id, sectionNum=f"{section_num:02d}"))
                
                # 更新进度
                ReportManager.update_progress(
                    report_id, "generating", 
                    base_progress + int(70 / total_sections),
                    t('progress.sectionDone', title=section.title),
                    current_section=None,
                    completed_sections=completed_section_titles
                )
            
            # 阶段3: 组装完整报告
            if progress_callback:
                progress_callback("generating", 95, t('progress.assemblingReport'))
            
            ReportManager.update_progress(
                report_id, "generating", 95, t('progress.assemblingReport'),
                completed_sections=completed_section_titles
            )
            
            # 使用ReportManager组装完整报告
            report.markdown_content = ReportManager.assemble_full_report(report_id, outline)
            report.status = ReportStatus.COMPLETED
            report.completed_at = datetime.now().isoformat()
            
            # 计算总耗时
            total_time_seconds = (datetime.now() - start_time).total_seconds()
            
            # 记录报告完成日志
            if self.report_logger:
                self.report_logger.log_report_complete(
                    total_sections=total_sections,
                    total_time_seconds=total_time_seconds
                )
            
            # 保存最终报告
            ReportManager.save_report(report)
            ReportManager.update_progress(
                report_id, "completed", 100, t('progress.reportComplete'),
                completed_sections=completed_section_titles
            )
            
            if progress_callback:
                progress_callback("completed", 100, t('progress.reportComplete'))
            
            logger.info(t('report.reportGenDone', reportId=report_id))
            
            # 关闭控制台日志记录器
            if self.console_logger:
                self.console_logger.close()
                self.console_logger = None
            
            return report
            
        except Exception as e:
            logger.error(t('report.reportGenFailed', error=str(e)))
            report.status = ReportStatus.FAILED
            report.error = str(e)
            
            # 记录错误日志
            if self.report_logger:
                self.report_logger.log_error(str(e), "failed")
            
            # 保存失败状态
            try:
                ReportManager.save_report(report)
                ReportManager.update_progress(
                    report_id, "failed", -1, t('progress.reportFailed', error=str(e)),
                    completed_sections=completed_section_titles
                )
            except Exception:
                pass  # 忽略保存失败的错误
            
            # 关闭控制台日志记录器
            if self.console_logger:
                self.console_logger.close()
                self.console_logger = None
            
            return report
    
    def chat(
        self, 
        message: str,
        chat_history: List[Dict[str, str]] = None
    ) -> Dict[str, Any]:
        """
        与Report Agent对话
        
        在对话中Agent可以自主调用检索工具来回答问题
        
        Args:
            message: 用户消息
            chat_history: 对话历史
            
        Returns:
            {
                "response": "Agent回复",
                "tool_calls": [调用的工具列表],
                "sources": [信息来源]
            }
        """
        logger.info(t('report.agentChat', message=message[:50]))
        
        chat_history = chat_history or []
        
        # 获取已生成的报告内容
        report_content = ""
        try:
            report = ReportManager.get_report_by_simulation(self.simulation_id)
            if report and report.markdown_content:
                # 限制报告长度，避免上下文过长
                report_content = report.markdown_content[:15000]
                if len(report.markdown_content) > 15000:
                    report_content += "\n\n... [报告内容已截断] ..."
        except Exception as e:
            logger.warning(t('report.fetchReportFailed', error=e))
        
        system_prompt = CHAT_SYSTEM_PROMPT_TEMPLATE.format(
            simulation_requirement=self.simulation_requirement,
            report_content=report_content if report_content else "（暂无报告）",
            tools_description=self._get_tools_description(),
        )
        system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"

        # 构建消息
        messages = [{"role": "system", "content": system_prompt}]
        
        # 添加历史对话
        for h in chat_history[-10:]:  # 限制历史长度
            messages.append(h)
        
        # 添加用户消息
        messages.append({
            "role": "user", 
            "content": message
        })
        
        # ReACT循环（简化版）
        tool_calls_made = []
        max_iterations = 2  # 减少迭代轮数
        
        for iteration in range(max_iterations):
            response = self.llm.chat(
                messages=messages,
                temperature=0.5
            )
            
            # 解析工具调用
            tool_calls = self._parse_tool_calls(response)
            
            if not tool_calls:
                # 没有工具调用，直接返回响应
                clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', response, flags=re.DOTALL)
                clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
                
                return {
                    "response": clean_response.strip(),
                    "tool_calls": tool_calls_made,
                    "sources": [tc.get("parameters", {}).get("query", "") for tc in tool_calls_made]
                }
            
            # 执行工具调用（限制数量）
            tool_results = []
            for call in tool_calls[:1]:  # 每轮最多执行1次工具调用
                if len(tool_calls_made) >= self.MAX_TOOL_CALLS_PER_CHAT:
                    break
                result = self._execute_tool(call["name"], call.get("parameters", {}))
                tool_results.append({
                    "tool": call["name"],
                    "result": result[:1500]  # 限制结果长度
                })
                tool_calls_made.append(call)
            
            # 将结果添加到消息
            messages.append({"role": "assistant", "content": response})
            observation = "\n".join([f"[{r['tool']}结果]\n{r['result']}" for r in tool_results])
            messages.append({
                "role": "user",
                "content": observation + CHAT_OBSERVATION_SUFFIX
            })
        
        # 达到最大迭代，获取最终响应
        final_response = self.llm.chat(
            messages=messages,
            temperature=0.5
        )
        
        # 清理响应
        clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', final_response, flags=re.DOTALL)
        clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
        
        return {
            "response": clean_response.strip(),
            "tool_calls": tool_calls_made,
            "sources": [tc.get("parameters", {}).get("query", "") for tc in tool_calls_made]
        }


class ReportManager:
    """
    报告管理器
    
    负责报告的持久化存储和检索
    
    文件结构（分章节输出）：
    reports/
      {report_id}/
        meta.json          - 报告元信息和状态
        outline.json       - 报告大纲
        progress.json      - 生成进度
        section_01.md      - 第1章节
        section_02.md      - 第2章节
        ...
        full_report.md     - 完整报告
    """
    
    # 报告存储目录
    REPORTS_DIR = os.path.join(Config.UPLOAD_FOLDER, 'reports')
    
    @classmethod
    def _ensure_reports_dir(cls):
        """确保报告根目录存在"""
        os.makedirs(cls.REPORTS_DIR, exist_ok=True)
    
    @classmethod
    def _get_report_folder(cls, report_id: str) -> str:
        """获取报告文件夹路径"""
        return os.path.join(cls.REPORTS_DIR, report_id)
    
    @classmethod
    def _ensure_report_folder(cls, report_id: str) -> str:
        """确保报告文件夹存在并返回路径"""
        folder = cls._get_report_folder(report_id)
        os.makedirs(folder, exist_ok=True)
        return folder
    
    @classmethod
    def _get_report_path(cls, report_id: str) -> str:
        """获取报告元信息文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "meta.json")
    
    @classmethod
    def _get_report_markdown_path(cls, report_id: str) -> str:
        """获取完整报告Markdown文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "full_report.md")
    
    @classmethod
    def _get_outline_path(cls, report_id: str) -> str:
        """获取大纲文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "outline.json")
    
    @classmethod
    def _get_progress_path(cls, report_id: str) -> str:
        """获取进度文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "progress.json")
    
    @classmethod
    def _get_section_path(cls, report_id: str, section_index: int) -> str:
        """获取章节Markdown文件路径"""
        return os.path.join(cls._get_report_folder(report_id), f"section_{section_index:02d}.md")
    
    @classmethod
    def _get_agent_log_path(cls, report_id: str) -> str:
        """获取 Agent 日志文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "agent_log.jsonl")
    
    @classmethod
    def _get_console_log_path(cls, report_id: str) -> str:
        """获取控制台日志文件路径"""
        return os.path.join(cls._get_report_folder(report_id), "console_log.txt")
    
    @classmethod
    def get_console_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
        """
        获取控制台日志内容
        
        这是报告生成过程中的控制台输出日志（INFO、WARNING等），
        与 agent_log.jsonl 的结构化日志不同。
        
        Args:
            report_id: 报告ID
            from_line: 从第几行开始读取（用于增量获取，0 表示从头开始）
            
        Returns:
            {
                "logs": [日志行列表],
                "total_lines": 总行数,
                "from_line": 起始行号,
                "has_more": 是否还有更多日志
            }
        """
        log_path = cls._get_console_log_path(report_id)
        
        if not os.path.exists(log_path):
            return {
                "logs": [],
                "total_lines": 0,
                "from_line": 0,
                "has_more": False
            }
        
        logs = []
        total_lines = 0
        
        with open(log_path, 'r', encoding='utf-8') as f:
            for i, line in enumerate(f):
                total_lines = i + 1
                if i >= from_line:
                    # 保留原始日志行，去掉末尾换行符
                    logs.append(line.rstrip('\n\r'))
        
        return {
            "logs": logs,
            "total_lines": total_lines,
            "from_line": from_line,
            "has_more": False  # 已读取到末尾
        }
    
    @classmethod
    def get_console_log_stream(cls, report_id: str) -> List[str]:
        """
        获取完整的控制台日志（一次性获取全部）
        
        Args:
            report_id: 报告ID
            
        Returns:
            日志行列表
        """
        result = cls.get_console_log(report_id, from_line=0)
        return result["logs"]
    
    @classmethod
    def get_agent_log(cls, report_id: str, from_line: int = 0) -> Dict[str, Any]:
        """
        获取 Agent 日志内容
        
        Args:
            report_id: 报告ID
            from_line: 从第几行开始读取（用于增量获取，0 表示从头开始）
            
        Returns:
            {
                "logs": [日志条目列表],
                "total_lines": 总行数,
                "from_line": 起始行号,
                "has_more": 是否还有更多日志
            }
        """
        log_path = cls._get_agent_log_path(report_id)
        
        if not os.path.exists(log_path):
            return {
                "logs": [],
                "total_lines": 0,
                "from_line": 0,
                "has_more": False
            }
        
        logs = []
        total_lines = 0
        
        with open(log_path, 'r', encoding='utf-8') as f:
            for i, line in enumerate(f):
                total_lines = i + 1
                if i >= from_line:
                    try:
                        log_entry = json.loads(line.strip())
                        logs.append(log_entry)
                    except json.JSONDecodeError:
                        # 跳过解析失败的行
                        continue
        
        return {
            "logs": logs,
            "total_lines": total_lines,
            "from_line": from_line,
            "has_more": False  # 已读取到末尾
        }
    
    @classmethod
    def get_agent_log_stream(cls, report_id: str) -> List[Dict[str, Any]]:
        """
        获取完整的 Agent 日志（用于一次性获取全部）
        
        Args:
            report_id: 报告ID
            
        Returns:
            日志条目列表
        """
        result = cls.get_agent_log(report_id, from_line=0)
        return result["logs"]
    
    @classmethod
    def save_outline(cls, report_id: str, outline: ReportOutline) -> None:
        """
        保存报告大纲
        
        在规划阶段完成后立即调用
        """
        cls._ensure_report_folder(report_id)
        
        with open(cls._get_outline_path(report_id), 'w', encoding='utf-8') as f:
            json.dump(outline.to_dict(), f, ensure_ascii=False, indent=2)
        
        logger.info(t('report.outlineSaved', reportId=report_id))
    
    @classmethod
    def save_section(
        cls,
        report_id: str,
        section_index: int,
        section: ReportSection
    ) -> str:
        """
        保存单个章节

        在每个章节生成完成后立即调用，实现分章节输出

        Args:
            report_id: 报告ID
            section_index: 章节索引（从1开始）
            section: 章节对象

        Returns:
            保存的文件路径
        """
        cls._ensure_report_folder(report_id)

        # 构建章节Markdown内容 - 清理可能存在的重复标题
        cleaned_content = cls._clean_section_content(section.content, section.title)
        md_content = f"## {section.title}\n\n"
        if cleaned_content:
            md_content += f"{cleaned_content}\n\n"

        # 保存文件
        file_suffix = f"section_{section_index:02d}.md"
        file_path = os.path.join(cls._get_report_folder(report_id), file_suffix)
        with open(file_path, 'w', encoding='utf-8') as f:
            f.write(md_content)

        logger.info(t('report.sectionFileSaved', reportId=report_id, fileSuffix=file_suffix))
        return file_path
    
    @classmethod
    def _clean_section_content(cls, content: str, section_title: str) -> str:
        """
        清理章节内容
        
        1. 移除内容开头与章节标题重复的Markdown标题行
        2. 将所有 ### 及以下级别的标题转换为粗体文本
        
        Args:
            content: 原始内容
            section_title: 章节标题
            
        Returns:
            清理后的内容
        """
        import re
        
        if not content:
            return content
        
        content = content.strip()
        lines = content.split('\n')
        cleaned_lines = []
        skip_next_empty = False
        
        for i, line in enumerate(lines):
            stripped = line.strip()
            
            # 检查是否是Markdown标题行
            heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
            
            if heading_match:
                level = len(heading_match.group(1))
                title_text = heading_match.group(2).strip()
                
                # 检查是否是与章节标题重复的标题（跳过前5行内的重复）
                if i < 5:
                    if title_text == section_title or title_text.replace(' ', '') == section_title.replace(' ', ''):
                        skip_next_empty = True
                        continue
                
                # 将所有级别的标题（#, ##, ###, ####等）转换为粗体
                # 因为章节标题由系统添加，内容中不应有任何标题
                cleaned_lines.append(f"**{title_text}**")
                cleaned_lines.append("")  # 添加空行
                continue
            
            # 如果上一行是被跳过的标题，且当前行为空，也跳过
            if skip_next_empty and stripped == '':
                skip_next_empty = False
                continue
            
            skip_next_empty = False
            cleaned_lines.append(line)
        
        # 移除开头的空行
        while cleaned_lines and cleaned_lines[0].strip() == '':
            cleaned_lines.pop(0)
        
        # 移除开头的分隔线
        while cleaned_lines and cleaned_lines[0].strip() in ['---', '***', '___']:
            cleaned_lines.pop(0)
            # 同时移除分隔线后的空行
            while cleaned_lines and cleaned_lines[0].strip() == '':
                cleaned_lines.pop(0)
        
        return '\n'.join(cleaned_lines)
    
    @classmethod
    def update_progress(
        cls, 
        report_id: str, 
        status: str, 
        progress: int, 
        message: str,
        current_section: str = None,
        completed_sections: List[str] = None
    ) -> None:
        """
        更新报告生成进度
        
        前端可以通过读取progress.json获取实时进度
        """
        cls._ensure_report_folder(report_id)
        
        progress_data = {
            "status": status,
            "progress": progress,
            "message": message,
            "current_section": current_section,
            "completed_sections": completed_sections or [],
            "updated_at": datetime.now().isoformat()
        }
        
        with open(cls._get_progress_path(report_id), 'w', encoding='utf-8') as f:
            json.dump(progress_data, f, ensure_ascii=False, indent=2)
    
    @classmethod
    def get_progress(cls, report_id: str) -> Optional[Dict[str, Any]]:
        """获取报告生成进度"""
        path = cls._get_progress_path(report_id)
        
        if not os.path.exists(path):
            return None
        
        with open(path, 'r', encoding='utf-8') as f:
            return json.load(f)
    
    @classmethod
    def get_generated_sections(cls, report_id: str) -> List[Dict[str, Any]]:
        """
        获取已生成的章节列表
        
        返回所有已保存的章节文件信息
        """
        folder = cls._get_report_folder(report_id)
        
        if not os.path.exists(folder):
            return []
        
        sections = []
        for filename in sorted(os.listdir(folder)):
            if filename.startswith('section_') and filename.endswith('.md'):
                file_path = os.path.join(folder, filename)
                with open(file_path, 'r', encoding='utf-8') as f:
                    content = f.read()

                # 从文件名解析章节索引
                parts = filename.replace('.md', '').split('_')
                section_index = int(parts[1])

                sections.append({
                    "filename": filename,
                    "section_index": section_index,
                    "content": content
                })

        return sections
    
    @classmethod
    def assemble_full_report(cls, report_id: str, outline: ReportOutline) -> str:
        """
        组装完整报告
        
        从已保存的章节文件组装完整报告，并进行标题清理
        """
        folder = cls._get_report_folder(report_id)
        
        # 构建报告头部
        md_content = f"# {outline.title}\n\n"
        md_content += f"> {outline.summary}\n\n"
        md_content += f"---\n\n"
        
        # 按顺序读取所有章节文件
        sections = cls.get_generated_sections(report_id)
        for section_info in sections:
            md_content += section_info["content"]
        
        # 后处理：清理整个报告的标题问题
        md_content = cls._post_process_report(md_content, outline)
        
        # 保存完整报告
        full_path = cls._get_report_markdown_path(report_id)
        with open(full_path, 'w', encoding='utf-8') as f:
            f.write(md_content)
        
        logger.info(t('report.fullReportAssembled', reportId=report_id))
        return md_content
    
    @classmethod
    def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
        """
        后处理报告内容
        
        1. 移除重复的标题
        2. 保留报告主标题(#)和章节标题(##)，移除其他级别的标题(###, ####等)
        3. 清理多余的空行和分隔线
        
        Args:
            content: 原始报告内容
            outline: 报告大纲
            
        Returns:
            处理后的内容
        """
        import re
        
        lines = content.split('\n')
        processed_lines = []
        prev_was_heading = False
        
        # 收集大纲中的所有章节标题
        section_titles = set()
        for section in outline.sections:
            section_titles.add(section.title)
        
        i = 0
        while i < len(lines):
            line = lines[i]
            stripped = line.strip()
            
            # 检查是否是标题行
            heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
            
            if heading_match:
                level = len(heading_match.group(1))
                title = heading_match.group(2).strip()
                
                # 检查是否是重复标题（在连续5行内出现相同内容的标题）
                is_duplicate = False
                for j in range(max(0, len(processed_lines) - 5), len(processed_lines)):
                    prev_line = processed_lines[j].strip()
                    prev_match = re.match(r'^(#{1,6})\s+(.+)$', prev_line)
                    if prev_match:
                        prev_title = prev_match.group(2).strip()
                        if prev_title == title:
                            is_duplicate = True
                            break
                
                if is_duplicate:
                    # 跳过重复标题及其后的空行
                    i += 1
                    while i < len(lines) and lines[i].strip() == '':
                        i += 1
                    continue
                
                # 标题层级处理：
                # - # (level=1) 只保留报告主标题
                # - ## (level=2) 保留章节标题
                # - ### 及以下 (level>=3) 转换为粗体文本
                
                if level == 1:
                    if title == outline.title:
                        # 保留报告主标题
                        processed_lines.append(line)
                        prev_was_heading = True
                    elif title in section_titles:
                        # 章节标题错误使用了#，修正为##
                        processed_lines.append(f"## {title}")
                        prev_was_heading = True
                    else:
                        # 其他一级标题转为粗体
                        processed_lines.append(f"**{title}**")
                        processed_lines.append("")
                        prev_was_heading = False
                elif level == 2:
                    if title in section_titles or title == outline.title:
                        # 保留章节标题
                        processed_lines.append(line)
                        prev_was_heading = True
                    else:
                        # 非章节的二级标题转为粗体
                        processed_lines.append(f"**{title}**")
                        processed_lines.append("")
                        prev_was_heading = False
                else:
                    # ### 及以下级别的标题转换为粗体文本
                    processed_lines.append(f"**{title}**")
                    processed_lines.append("")
                    prev_was_heading = False
                
                i += 1
                continue
            
            elif stripped == '---' and prev_was_heading:
                # 跳过标题后紧跟的分隔线
                i += 1
                continue
            
            elif stripped == '' and prev_was_heading:
                # 标题后只保留一个空行
                if processed_lines and processed_lines[-1].strip() != '':
                    processed_lines.append(line)
                prev_was_heading = False
            
            else:
                processed_lines.append(line)
                prev_was_heading = False
            
            i += 1
        
        # 清理连续的多个空行（保留最多2个）
        result_lines = []
        empty_count = 0
        for line in processed_lines:
            if line.strip() == '':
                empty_count += 1
                if empty_count <= 2:
                    result_lines.append(line)
            else:
                empty_count = 0
                result_lines.append(line)
        
        return '\n'.join(result_lines)
    
    @classmethod
    def save_report(cls, report: Report) -> None:
        """保存报告元信息和完整报告"""
        cls._ensure_report_folder(report.report_id)
        
        # 保存元信息JSON
        with open(cls._get_report_path(report.report_id), 'w', encoding='utf-8') as f:
            json.dump(report.to_dict(), f, ensure_ascii=False, indent=2)
        
        # 保存大纲
        if report.outline:
            cls.save_outline(report.report_id, report.outline)
        
        # 保存完整Markdown报告
        if report.markdown_content:
            with open(cls._get_report_markdown_path(report.report_id), 'w', encoding='utf-8') as f:
                f.write(report.markdown_content)
        
        logger.info(t('report.reportSaved', reportId=report.report_id))
    
    @classmethod
    def get_report(cls, report_id: str) -> Optional[Report]:
        """获取报告"""
        path = cls._get_report_path(report_id)
        
        if not os.path.exists(path):
            # 兼容旧格式：检查直接存储在reports目录下的文件
            old_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
            if os.path.exists(old_path):
                path = old_path
            else:
                return None
        
        with open(path, 'r', encoding='utf-8') as f:
            data = json.load(f)
        
        # 重建Report对象
        outline = None
        if data.get('outline'):
            outline_data = data['outline']
            sections = []
            for s in outline_data.get('sections', []):
                sections.append(ReportSection(
                    title=s['title'],
                    content=s.get('content', '')
                ))
            outline = ReportOutline(
                title=outline_data['title'],
                summary=outline_data['summary'],
                sections=sections
            )
        
        # 如果markdown_content为空，尝试从full_report.md读取
        markdown_content = data.get('markdown_content', '')
        if not markdown_content:
            full_report_path = cls._get_report_markdown_path(report_id)
            if os.path.exists(full_report_path):
                with open(full_report_path, 'r', encoding='utf-8') as f:
                    markdown_content = f.read()
        
        return Report(
            report_id=data['report_id'],
            simulation_id=data['simulation_id'],
            graph_id=data['graph_id'],
            simulation_requirement=data['simulation_requirement'],
            status=ReportStatus(data['status']),
            outline=outline,
            markdown_content=markdown_content,
            created_at=data.get('created_at', ''),
            completed_at=data.get('completed_at', ''),
            error=data.get('error')
        )
    
    @classmethod
    def get_report_by_simulation(cls, simulation_id: str) -> Optional[Report]:
        """根据模拟ID获取报告"""
        cls._ensure_reports_dir()
        
        for item in os.listdir(cls.REPORTS_DIR):
            item_path = os.path.join(cls.REPORTS_DIR, item)
            # 新格式：文件夹
            if os.path.isdir(item_path):
                report = cls.get_report(item)
                if report and report.simulation_id == simulation_id:
                    return report
            # 兼容旧格式：JSON文件
            elif item.endswith('.json'):
                report_id = item[:-5]
                report = cls.get_report(report_id)
                if report and report.simulation_id == simulation_id:
                    return report
        
        return None
    
    @classmethod
    def list_reports(cls, simulation_id: Optional[str] = None, limit: int = 50) -> List[Report]:
        """列出报告"""
        cls._ensure_reports_dir()
        
        reports = []
        for item in os.listdir(cls.REPORTS_DIR):
            item_path = os.path.join(cls.REPORTS_DIR, item)
            # 新格式：文件夹
            if os.path.isdir(item_path):
                report = cls.get_report(item)
                if report:
                    if simulation_id is None or report.simulation_id == simulation_id:
                        reports.append(report)
            # 兼容旧格式：JSON文件
            elif item.endswith('.json'):
                report_id = item[:-5]
                report = cls.get_report(report_id)
                if report:
                    if simulation_id is None or report.simulation_id == simulation_id:
                        reports.append(report)
        
        # 按创建时间倒序
        reports.sort(key=lambda r: r.created_at, reverse=True)
        
        return reports[:limit]
    
    @classmethod
    def delete_report(cls, report_id: str) -> bool:
        """删除报告（整个文件夹）"""
        import shutil
        
        folder_path = cls._get_report_folder(report_id)
        
        # 新格式：删除整个文件夹
        if os.path.exists(folder_path) and os.path.isdir(folder_path):
            shutil.rmtree(folder_path)
            logger.info(t('report.reportFolderDeleted', reportId=report_id))
            return True
        
        # 兼容旧格式：删除单独的文件
        deleted = False
        old_json_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
        old_md_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.md")
        
        if os.path.exists(old_json_path):
            os.remove(old_json_path)
            deleted = True
        if os.path.exists(old_md_path):
            os.remove(old_md_path)
            deleted = True
        
        return deleted