2570 lines
99 KiB
Python
2570 lines
99 KiB
Python
"""
|
||
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 .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
|
||
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||
Args:
|
||
report_id: 报告ID，用于确定日志文件路径
|
||
"""
|
||
self.report_id = report_id
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||
self.log_file_path = os.path.join(
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||
Config.UPLOAD_FOLDER, 'reports', report_id, 'agent_log.jsonl'
|
||
)
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||
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"""
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||
log_dir = os.path.dirname(self.log_file_path)
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||
os.makedirs(log_dir, exist_ok=True)
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||
|
||
def _get_elapsed_time(self) -> float:
|
||
"""获取从开始到现在的耗时（秒）"""
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||
return (datetime.now() - self.start_time).total_seconds()
|
||
|
||
def log(
|
||
self,
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||
action: str,
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||
stage: str,
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||
details: Dict[str, Any],
|
||
section_title: str = None,
|
||
section_index: int = None
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||
):
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||
"""
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||
记录一条日志
|
||
|
||
Args:
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||
action: 动作类型，如 'start', 'tool_call', 'llm_response', 'section_complete' 等
|
||
stage: 当前阶段，如 'planning', 'generating', 'completed'
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||
details: 详细内容字典，不截断
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||
section_title: 当前章节标题（可选）
|
||
section_index: 当前章节索引（可选）
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||
"""
|
||
log_entry = {
|
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"timestamp": datetime.now().isoformat(),
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"elapsed_seconds": round(self._get_elapsed_time(), 2),
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||
"report_id": self.report_id,
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||
"action": action,
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||
"stage": stage,
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||
"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:
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f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
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||
|
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def log_start(self, simulation_id: str, graph_id: str, simulation_requirement: str):
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||
"""记录报告生成开始"""
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||
self.log(
|
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action="report_start",
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||
stage="pending",
|
||
details={
|
||
"simulation_id": simulation_id,
|
||
"graph_id": graph_id,
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||
"simulation_requirement": simulation_requirement,
|
||
"message": "报告生成任务开始"
|
||
}
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||
)
|
||
|
||
def log_planning_start(self):
|
||
"""记录大纲规划开始"""
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||
self.log(
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||
action="planning_start",
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||
stage="planning",
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details={"message": "开始规划报告大纲"}
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||
)
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||
|
||
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",
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||
stage="planning",
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||
details={
|
||
"message": "获取模拟上下文信息",
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||
"context": context
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||
}
|
||
)
|
||
|
||
def log_planning_complete(self, outline_dict: Dict[str, Any]):
|
||
"""记录大纲规划完成"""
|
||
self.log(
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||
action="planning_complete",
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||
stage="planning",
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||
details={
|
||
"message": "大纲规划完成",
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||
"outline": outline_dict
|
||
}
|
||
)
|
||
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||
def log_section_start(self, section_title: str, section_index: int):
|
||
"""记录章节生成开始"""
|
||
self.log(
|
||
action="section_start",
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||
stage="generating",
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||
section_title=section_title,
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||
section_index=section_index,
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||
details={"message": f"开始生成章节: {section_title}"}
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||
)
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||
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def log_react_thought(self, section_title: str, section_index: int, iteration: int, thought: str):
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||
"""记录 ReACT 思考过程"""
|
||
self.log(
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||
action="react_thought",
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||
stage="generating",
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||
section_title=section_title,
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||
section_index=section_index,
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||
details={
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||
"iteration": iteration,
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||
"thought": thought,
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||
"message": f"ReACT 第{iteration}轮思考"
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||
}
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||
)
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||
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||
def log_tool_call(
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||
self,
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||
section_title: str,
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||
section_index: int,
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||
tool_name: str,
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||
parameters: Dict[str, Any],
|
||
iteration: int
|
||
):
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||
"""记录工具调用"""
|
||
self.log(
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||
action="tool_call",
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||
stage="generating",
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||
section_title=section_title,
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||
section_index=section_index,
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||
details={
|
||
"iteration": iteration,
|
||
"tool_name": tool_name,
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||
"parameters": parameters,
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||
"message": f"调用工具: {tool_name}"
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||
}
|
||
)
|
||
|
||
def log_tool_result(
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||
self,
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||
section_title: str,
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||
section_index: int,
|
||
tool_name: str,
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||
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,
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||
section_index=section_index,
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||
details={
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||
"iteration": iteration,
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||
"tool_name": tool_name,
|
||
"result": result, # 完整结果，不截断
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||
"result_length": len(result),
|
||
"message": f"工具 {tool_name} 返回结果"
|
||
}
|
||
)
|
||
|
||
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,
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||
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": f"LLM 响应 (工具调用: {has_tool_calls}, 最终答案: {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": f"章节 {section_title} 内容生成完成"
|
||
}
|
||
)
|
||
|
||
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": f"章节 {section_title} 生成完成"
|
||
}
|
||
)
|
||
|
||
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": "报告生成完成"
|
||
}
|
||
)
|
||
|
||
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": f"发生错误: {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 Retrieval - Powerful Retrieval Tool]
|
||
This is our powerful retrieval function, designed for deep analysis. It will:
|
||
1. Automatically decompose your question into multiple sub-questions
|
||
2. Retrieve information from the simulation graph across multiple dimensions
|
||
3. Integrate results from semantic search, entity analysis, and relationship chain tracking
|
||
4. Return the most comprehensive and in-depth retrieval content
|
||
|
||
[Use Cases]
|
||
- Need to deeply analyze a topic
|
||
- Need to understand multiple aspects of an event
|
||
- Need to obtain rich material to support report sections
|
||
|
||
[Return Content]
|
||
- Original relevant facts (can be directly quoted)
|
||
- Core entity insights
|
||
- Relationship chain analysis"""
|
||
|
||
TOOL_DESC_PANORAMA_SEARCH = """\
|
||
[Broad Search - Full Overview]
|
||
This tool is used to obtain a complete overview of simulation results, particularly suited for understanding event evolution. It will:
|
||
1. Retrieve all related nodes and relationships
|
||
2. Distinguish between currently valid facts and historical/expired facts
|
||
3. Help you understand how public opinion evolved
|
||
|
||
[Use Cases]
|
||
- Need to understand the complete development timeline of events
|
||
- Need to compare opinion changes across different stages
|
||
- Need to obtain comprehensive entity and relationship information
|
||
|
||
[Return Content]
|
||
- Currently valid facts (latest simulation results)
|
||
- Historical/expired facts (evolution records)
|
||
- All involved entities"""
|
||
|
||
TOOL_DESC_QUICK_SEARCH = """\
|
||
[Simple Search - Quick Retrieval]
|
||
A lightweight quick retrieval tool, suitable for simple, direct information queries.
|
||
|
||
[Use Cases]
|
||
- Need to quickly find specific information
|
||
- Need to verify a fact
|
||
- Simple information retrieval
|
||
|
||
[Return Content]
|
||
- List of facts most relevant to the query"""
|
||
|
||
TOOL_DESC_INTERVIEW_AGENTS = """\
|
||
[Deep Interview - Real Agent Interview (Dual Platform)]
|
||
Calls the OASIS simulation environment's interview API to conduct real interviews with running simulation Agents!
|
||
This is not an LLM simulation, but calls the real interview interface to obtain original answers from simulation Agents.
|
||
By default, interviews are conducted simultaneously on both Twitter and Reddit platforms for more comprehensive perspectives.
|
||
|
||
Workflow:
|
||
1. Automatically reads persona files to understand all simulation Agents
|
||
2. Intelligently selects Agents most relevant to the interview topic (e.g., students, media, officials)
|
||
3. Automatically generates interview questions
|
||
4. Calls /api/simulation/interview/batch interface for real interviews on both platforms
|
||
5. Integrates all interview results, providing multi-perspective analysis
|
||
|
||
[Use Cases]
|
||
- Need to understand event perspectives from different roles (How do students see it? How does media cover it? What do officials say?)
|
||
- Need to collect opinions and stances from multiple parties
|
||
- Need to obtain real answers from simulation Agents (from the OASIS simulation environment)
|
||
- Want to make the report more vivid with "interview transcripts"
|
||
|
||
[Return Content]
|
||
- Identity information of interviewed Agents
|
||
- Each Agent's interview responses on both Twitter and Reddit platforms
|
||
- Key quotes (can be directly cited)
|
||
- Interview summary and viewpoint comparison
|
||
|
||
[Important] The OASIS simulation environment must be running to use this feature!"""
|
||
|
||
# ── 大纲规划 prompt ──
|
||
|
||
PLAN_SYSTEM_PROMPT = """\
|
||
You are an expert at writing "Future Prediction Reports," with a "god's eye view" of the simulated world — you can observe every Agent's behavior, statements, and interactions within the simulation.
|
||
|
||
[Core Concept]
|
||
We built a simulated world and injected specific "simulation requirements" as variables. The evolution results of the simulated world are predictions of what may happen in the future. What you are observing is not "experimental data," but a "rehearsal of the future."
|
||
|
||
[Your Task]
|
||
Write a "Future Prediction Report" that answers:
|
||
1. Under the conditions we set, what happened in the future?
|
||
2. How did various types of Agents (groups) react and act?
|
||
3. What noteworthy future trends and risks does this simulation reveal?
|
||
|
||
[Report Positioning]
|
||
- This is a simulation-based future prediction report, revealing "if this happens, what will the future look like"
|
||
- Focus on prediction results: event trajectories, group reactions, emergent phenomena, potential risks
|
||
- Agent behaviors and statements in the simulated world are predictions of future human behavior
|
||
- This is NOT an analysis of the current real world
|
||
- This is NOT a generic public opinion summary
|
||
|
||
[Section Count Limits]
|
||
- Minimum 2 sections, maximum 5 sections
|
||
- No sub-sections needed, each section should contain complete content
|
||
- Content should be concise, focused on core prediction findings
|
||
- Section structure should be designed by you based on prediction results
|
||
|
||
Output a JSON-formatted report outline in the following format:
|
||
{
|
||
"title": "Report Title",
|
||
"summary": "Report summary (one sentence summarizing core prediction findings)",
|
||
"sections": [
|
||
{
|
||
"title": "Section Title",
|
||
"description": "Section content description"
|
||
}
|
||
]
|
||
}
|
||
|
||
Note: the sections array must have at least 2 and at most 5 elements!"""
|
||
|
||
PLAN_USER_PROMPT_TEMPLATE = """\
|
||
[Prediction Scenario Setup]
|
||
Variable injected into the simulated world (simulation requirement): {simulation_requirement}
|
||
|
||
[Simulated World Scale]
|
||
- Number of entities in simulation: {total_nodes}
|
||
- Number of relationships between entities: {total_edges}
|
||
- Entity type distribution: {entity_types}
|
||
- Number of active Agents: {total_entities}
|
||
|
||
[Sample Future Facts Predicted by Simulation]
|
||
{related_facts_json}
|
||
|
||
Examine this future rehearsal from a "god's eye view":
|
||
1. Under the conditions we set, what state did the future present?
|
||
2. How did various groups (Agents) react and act?
|
||
3. What noteworthy future trends does this simulation reveal?
|
||
|
||
Design the most appropriate report section structure based on prediction results.
|
||
|
||
[Reminder] Report section count: minimum 2, maximum 5, content should be concise and focused on core prediction findings."""
|
||
|
||
# ── 章节生成 prompt ──
|
||
|
||
SECTION_SYSTEM_PROMPT_TEMPLATE = """\
|
||
You are an expert at writing "Future Prediction Reports," currently writing one section of the report.
|
||
|
||
Report Title: {report_title}
|
||
Report Summary: {report_summary}
|
||
Prediction Scenario (Simulation Requirement): {simulation_requirement}
|
||
|
||
Current Section to Write: {section_title}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Core Concept]
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
The simulated world is a rehearsal of the future. We injected specific conditions (simulation requirements) into the simulated world. The behaviors and interactions of Agents in the simulation are predictions of future human behavior.
|
||
|
||
Your task is to:
|
||
- Reveal what happened in the future under the set conditions
|
||
- Predict how various groups (Agents) reacted and acted
|
||
- Discover noteworthy future trends, risks, and opportunities
|
||
|
||
Do NOT write this as an analysis of the current real world.
|
||
DO focus on "what will the future look like" — simulation results ARE the predicted future.
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Most Important Rules - Must Follow]
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
1. [Must Call Tools to Observe the Simulated World]
|
||
- You are observing a future rehearsal from a "god's eye view"
|
||
- All content must come from events and Agent behaviors in the simulated world
|
||
- Do NOT use your own knowledge to write report content
|
||
- Each section must call tools at least 3 times (maximum 5) to observe the simulated world, which represents the future
|
||
|
||
2. [Must Quote Agents' Original Behaviors and Statements]
|
||
- Agent statements and behaviors are predictions of future human behavior
|
||
- Use quote format in the report to display these predictions, for example:
|
||
> "A certain group would say: original content..."
|
||
- These quotes are the core evidence of simulation predictions
|
||
|
||
3. [Language Consistency - Report Language]
|
||
- Tool-returned content may contain mixed languages
|
||
- The report should be written in a language consistent with the simulation requirement
|
||
- When quoting tool-returned content, translate it into the report language for fluency
|
||
- Maintain original meaning while ensuring natural expression
|
||
- This rule applies to both body text and quoted blocks (> format)
|
||
|
||
4. [Faithfully Present Prediction Results]
|
||
- Report content must reflect simulation results representing the future
|
||
- Do not add information that does not exist in the simulation
|
||
- If information is insufficient in some area, state it honestly
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Format Specifications - Extremely Important!]
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
[One Section = Minimum Content Unit]
|
||
- Each section is the smallest chunk unit of the report
|
||
- Do NOT use any Markdown headings (#, ##, ###, #### etc.) within sections
|
||
- Do NOT add a section title at the beginning of content
|
||
- Section titles are automatically added by the system, you only need to write body text
|
||
- Use **bold**, paragraph breaks, quotes, and lists to organize content, but NO headings
|
||
|
||
[Correct Example]
|
||
```
|
||
This section analyzes the public opinion propagation dynamics. Through in-depth analysis of simulation data, we found...
|
||
|
||
**Initial Breakout Phase**
|
||
|
||
Platform A served as the primary venue, carrying the core function of initial information release:
|
||
|
||
> "Platform A contributed 68% of initial voice volume..."
|
||
|
||
**Emotion Amplification Phase**
|
||
|
||
Platform B further amplified the event's impact:
|
||
|
||
- Strong visual impact
|
||
- High emotional resonance
|
||
```
|
||
|
||
[Incorrect Example]
|
||
```
|
||
## Executive Summary <- Wrong! Do not add any headings
|
||
### 1. Initial Phase <- Wrong! Do not use ### for sub-sections
|
||
#### 1.1 Detailed Analysis <- Wrong! Do not use #### for details
|
||
|
||
This section analyzes...
|
||
```
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Available Retrieval Tools] (call 3-5 times per section)
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
{tools_description}
|
||
|
||
[Tool Usage Suggestions - Mix different tools, don't use only one]
|
||
- insight_forge: Deep insight analysis, auto-decomposes questions and retrieves facts and relationships across multiple dimensions
|
||
- panorama_search: Wide-angle panoramic search, understand full event picture, timeline, and evolution
|
||
- quick_search: Quick verification of a specific information point
|
||
- interview_agents: Interview simulation Agents, get first-person perspectives and real reactions from different roles
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Workflow]
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
Each reply you can only do ONE of the following two things (not both):
|
||
|
||
Option A - Call a tool:
|
||
Output your thinking, then call a tool using the following format:
|
||
<tool_call>
|
||
{{"name": "tool_name", "parameters": {{"param_name": "param_value"}}}}
|
||
</tool_call>
|
||
The system will execute the tool and return results to you. You do not need to and cannot write tool return results yourself.
|
||
|
||
Option B - Output final content:
|
||
When you have obtained sufficient information through tools, output section content starting with "Final Answer:".
|
||
|
||
Strictly prohibited:
|
||
- Do NOT include both tool calls and Final Answer in a single reply
|
||
- Do NOT fabricate tool return results (Observation); all tool results are injected by the system
|
||
- Maximum one tool call per reply
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Section Content Requirements]
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
1. Content must be based on simulation data retrieved by tools
|
||
2. Quote original text extensively to demonstrate simulation effects
|
||
3. Use Markdown format (but NO headings):
|
||
- Use **bold text** to mark key points (instead of sub-headings)
|
||
- Use lists (- or 1.2.3.) to organize points
|
||
- Use blank lines to separate paragraphs
|
||
- Do NOT use #, ##, ###, #### or any heading syntax
|
||
4. [Quote Format - Must Be Standalone Paragraphs]
|
||
Quotes must be standalone paragraphs with blank lines before and after, not mixed into paragraphs:
|
||
|
||
Correct format:
|
||
```
|
||
The response was considered lacking in substance.
|
||
|
||
> "The response pattern appeared rigid and sluggish in the fast-changing social media environment."
|
||
|
||
This assessment reflects widespread public dissatisfaction.
|
||
```
|
||
|
||
Incorrect format:
|
||
```
|
||
The response was considered lacking. > "The response pattern..." This assessment reflects...
|
||
```
|
||
5. Maintain logical coherence with other sections
|
||
6. [Avoid Repetition] Carefully read the completed sections below, do not repeat the same information
|
||
7. [Emphasis] Do not add any headings! Use **bold** instead of sub-section headings"""
|
||
|
||
SECTION_USER_PROMPT_TEMPLATE = """\
|
||
Completed section content (read carefully to avoid repetition):
|
||
{previous_content}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
[Current Task] Write section: {section_title}
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
[Important Reminders]
|
||
1. Carefully read the completed sections above, avoid repeating the same content!
|
||
2. You must call tools to retrieve simulation data before starting
|
||
3. Mix different tools, don't use only one type
|
||
4. Report content must come from retrieval results, do not use your own knowledge
|
||
|
||
[Format Warning - Must Follow]
|
||
- Do NOT write any headings (#, ##, ###, #### are all prohibited)
|
||
- Do NOT write "{section_title}" as the opening
|
||
- Section titles are automatically added by the system
|
||
- Write body text directly, use **bold** instead of sub-section headings
|
||
|
||
Begin:
|
||
1. First think (Thought) about what information this section needs
|
||
2. Then call tools (Action) to retrieve simulation data
|
||
3. After collecting sufficient information, output Final Answer (body text only, no headings)"""
|
||
|
||
# ── ReACT 循环内消息模板 ──
|
||
|
||
REACT_OBSERVATION_TEMPLATE = """\
|
||
Observation (retrieval results):
|
||
|
||
═══ Tool {tool_name} returned ═══
|
||
{result}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
Tools called {tool_calls_count}/{max_tool_calls} times (used: {used_tools_str}){unused_hint}
|
||
- If information is sufficient: output section content starting with "Final Answer:" (must quote the above original text)
|
||
- If more information is needed: call a tool to continue retrieval
|
||
═══════════════════════════════════════════════════════════════"""
|
||
|
||
REACT_INSUFFICIENT_TOOLS_MSG = (
|
||
"[Notice] You have only called tools {tool_calls_count} times, at least {min_tool_calls} times required. "
|
||
"Please call more tools to retrieve additional simulation data before outputting Final Answer. {unused_hint}"
|
||
)
|
||
|
||
REACT_INSUFFICIENT_TOOLS_MSG_ALT = (
|
||
"Currently only {tool_calls_count} tool calls made, at least {min_tool_calls} required. "
|
||
"Please call tools to retrieve simulation data. {unused_hint}"
|
||
)
|
||
|
||
REACT_TOOL_LIMIT_MSG = (
|
||
"Tool call limit reached ({tool_calls_count}/{max_tool_calls}), no more tool calls allowed. "
|
||
'Please immediately output section content starting with "Final Answer:" based on information already obtained.'
|
||
)
|
||
|
||
REACT_UNUSED_TOOLS_HINT = "\nHint: You haven't used: {unused_list} yet. Consider trying different tools for multi-perspective information."
|
||
|
||
REACT_FORCE_FINAL_MSG = "Tool call limit reached. Please output Final Answer: and generate section content immediately."
|
||
|
||
# ── Chat prompt ──
|
||
|
||
CHAT_SYSTEM_PROMPT_TEMPLATE = """\
|
||
You are a concise and efficient simulation prediction assistant.
|
||
|
||
[Background]
|
||
Prediction conditions: {simulation_requirement}
|
||
|
||
[Generated Analysis Report]
|
||
{report_content}
|
||
|
||
[Rules]
|
||
1. Prioritize answering questions based on the report content above
|
||
2. Answer questions directly, avoid lengthy deliberation
|
||
3. Only call tools to retrieve more data when the report content is insufficient
|
||
4. Answers should be concise, clear, and well-organized
|
||
|
||
[Available Tools] (use only when needed, maximum 1-2 calls)
|
||
{tools_description}
|
||
|
||
[Tool Call Format]
|
||
<tool_call>
|
||
{{"name": "tool_name", "parameters": {{"param_name": "param_value"}}}}
|
||
</tool_call>
|
||
|
||
[Answer Style]
|
||
- Concise and direct, no lengthy essays
|
||
- Use > format to quote key content
|
||
- Give conclusions first, then explain reasons"""
|
||
|
||
CHAT_OBSERVATION_SUFFIX = "\n\nPlease answer the question concisely."
|
||
|
||
|
||
# ═══════════════════════════════════════════════════════════════
|
||
# 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(f"ReportAgent 初始化完成: graph_id={graph_id}, simulation_id={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": "The question or topic you want to deeply analyze",
|
||
"report_context": "Current report section context (optional, helps generate more precise sub-questions)"
|
||
}
|
||
},
|
||
"panorama_search": {
|
||
"name": "panorama_search",
|
||
"description": TOOL_DESC_PANORAMA_SEARCH,
|
||
"parameters": {
|
||
"query": "Search query for relevance ranking",
|
||
"include_expired": "Whether to include expired/historical content (default True)"
|
||
}
|
||
},
|
||
"quick_search": {
|
||
"name": "quick_search",
|
||
"description": TOOL_DESC_QUICK_SEARCH,
|
||
"parameters": {
|
||
"query": "Search query string",
|
||
"limit": "Number of results to return (optional, default 10)"
|
||
}
|
||
},
|
||
"interview_agents": {
|
||
"name": "interview_agents",
|
||
"description": TOOL_DESC_INTERVIEW_AGENTS,
|
||
"parameters": {
|
||
"interview_topic": "Interview topic or requirement description (e.g., 'understand students' views on the dormitory formaldehyde incident')",
|
||
"max_agents": "Maximum number of Agents to interview (optional, 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(f"执行工具: {tool_name}, 参数: {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("search_graph 已重定向到 quick_search")
|
||
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("get_simulation_context 已重定向到 insight_forge")
|
||
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"Unknown tool: {tool_name}. Please use one of: insight_forge, panorama_search, quick_search"
|
||
|
||
except Exception as e:
|
||
logger.error(f"工具执行失败: {tool_name}, 错误: {str(e)}")
|
||
return f"Tool execution failed: {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" Parameters: {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("开始规划报告大纲...")
|
||
|
||
if progress_callback:
|
||
progress_callback("planning", 0, "Analyzing simulation requirements...")
|
||
|
||
# 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, "Generating report outline...")
|
||
|
||
system_prompt = PLAN_SYSTEM_PROMPT
|
||
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, "Parsing outline structure...")
|
||
|
||
# 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, "Outline planning complete")
|
||
|
||
logger.info(f"大纲规划完成: {len(sections)} 个章节")
|
||
return outline
|
||
|
||
except Exception as e:
|
||
logger.error(f"大纲规划失败: {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 predictions",
|
||
sections=[
|
||
ReportSection(title="Prediction Scenario and Core Findings"),
|
||
ReportSection(title="Group Behavior Prediction Analysis"),
|
||
ReportSection(title="Trend Outlook and Risk Indicators")
|
||
]
|
||
)
|
||
|
||
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(f"ReACT生成章节: {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(),
|
||
)
|
||
|
||
# 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 = "(This is the first section)"
|
||
|
||
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),
|
||
f"Deep retrieval and writing ({tool_calls_count}/{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(f"章节 {section.title} 第 {iteration + 1} 次迭代: LLM 返回 None")
|
||
# 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 generating content."})
|
||
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(
|
||
f"章节 {section.title} 第 {iteration+1} 轮: "
|
||
f"LLM 同时输出工具调用和 Final Answer（第 {conflict_retries} 次冲突）"
|
||
)
|
||
|
||
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] You included both a tool call and Final Answer in one reply, which is not allowed.\n"
|
||
"Each reply can only do one of the following:\n"
|
||
"- Call a tool (output a <tool_call> block, do not write Final Answer)\n"
|
||
"- Output final content (start with 'Final Answer:', do not include <tool_call>)\n"
|
||
"Please reply again, doing only one of these."
|
||
),
|
||
})
|
||
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(
|
||
f"章节 {section.title}: 连续 {conflict_retries} 次冲突，"
|
||
"降级为截断执行第一个工具调用"
|
||
)
|
||
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"(These tools haven't been used yet, try them: {', '.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(f"章节 {section.title} 生成完成（工具调用: {tool_calls_count}次）")
|
||
|
||
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(f"LLM 尝试调用 {len(tool_calls)} 个工具，只执行第一个: {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"(These tools haven't been used yet, try them: {', '.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(f"章节 {section.title} 未检测到 'Final Answer:' 前缀，直接采纳LLM输出作为最终内容（工具调用: {tool_calls_count}次）")
|
||
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(f"章节 {section.title} 达到最大迭代次数，强制生成")
|
||
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(f"章节 {section.title} 强制收尾时 LLM 返回 None，使用默认错误提示")
|
||
final_answer = f"(This section failed to generate: LLM returned an empty response, please try again later)"
|
||
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, "Initializing report...",
|
||
completed_sections=[]
|
||
)
|
||
ReportManager.save_report(report)
|
||
|
||
# 阶段1: 规划大纲
|
||
report.status = ReportStatus.PLANNING
|
||
ReportManager.update_progress(
|
||
report_id, "planning", 5, "Starting report outline planning...",
|
||
completed_sections=[]
|
||
)
|
||
|
||
# 记录规划开始日志
|
||
self.report_logger.log_planning_start()
|
||
|
||
if progress_callback:
|
||
progress_callback("planning", 0, "Starting report outline planning...")
|
||
|
||
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, f"Outline planning complete, {len(outline.sections)} sections total",
|
||
completed_sections=[]
|
||
)
|
||
ReportManager.save_report(report)
|
||
|
||
logger.info(f"大纲已保存到文件: {report_id}/outline.json")
|
||
|
||
# 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,
|
||
f"Generating section: {section.title} ({section_num}/{total_sections})",
|
||
current_section=section.title,
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
if progress_callback:
|
||
progress_callback(
|
||
"generating",
|
||
base_progress,
|
||
f"Generating section: {section.title} ({section_num}/{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(f"章节已保存: {report_id}/section_{section_num:02d}.md")
|
||
|
||
# 更新进度
|
||
ReportManager.update_progress(
|
||
report_id, "generating",
|
||
base_progress + int(70 / total_sections),
|
||
f"Section {section.title} completed",
|
||
current_section=None,
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
# 阶段3: 组装完整报告
|
||
if progress_callback:
|
||
progress_callback("generating", 95, "Assembling full report...")
|
||
|
||
ReportManager.update_progress(
|
||
report_id, "generating", 95, "Assembling full report...",
|
||
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, "Report generation complete",
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
if progress_callback:
|
||
progress_callback("completed", 100, "Report generation complete")
|
||
|
||
logger.info(f"报告生成完成: {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(f"报告生成失败: {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, f"Report generation failed: {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(f"Report Agent对话: {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... [Report content truncated] ..."
|
||
except Exception as e:
|
||
logger.warning(f"获取报告内容失败: {e}")
|
||
|
||
system_prompt = CHAT_SYSTEM_PROMPT_TEMPLATE.format(
|
||
simulation_requirement=self.simulation_requirement,
|
||
report_content=report_content if report_content else "(No report available yet)",
|
||
tools_description=self._get_tools_description(),
|
||
)
|
||
|
||
# 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']} result]\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(f"大纲已保存: {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(f"章节已保存: {report_id}/{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(f"完整报告已组装: {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(f"报告已保存: {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(f"报告文件夹已删除: {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
|