2411 lines
89 KiB
Python
2411 lines
89 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
|
||
|
||
Args:
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||
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"""
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||
log_dir = os.path.dirname(self.log_file_path)
|
||
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）"""
|
||
return (datetime.now() - self.start_time).total_seconds()
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||
|
||
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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||
"""
|
||
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）
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||
section_index: 当前章节索引（可选）
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||
"""
|
||
log_entry = {
|
||
"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
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||
}
|
||
|
||
# 追加写入 JSONL 文件
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||
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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||
|
||
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",
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||
stage="pending",
|
||
details={
|
||
"simulation_id": simulation_id,
|
||
"graph_id": graph_id,
|
||
"simulation_requirement": simulation_requirement,
|
||
"message": "报告生成任务开始"
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||
}
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||
)
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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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||
)
|
||
|
||
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": "获取模拟上下文信息",
|
||
"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",
|
||
stage="planning",
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||
details={
|
||
"message": "大纲规划完成",
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||
"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,
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||
section_index=section_index,
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||
details={"message": f"开始生成章节: {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",
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||
section_title=section_title,
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||
section_index=section_index,
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||
details={
|
||
"iteration": iteration,
|
||
"thought": thought,
|
||
"message": f"ReACT 第{iteration}轮思考"
|
||
}
|
||
)
|
||
|
||
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",
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||
section_title=section_title,
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||
section_index=section_index,
|
||
details={
|
||
"iteration": iteration,
|
||
"tool_name": tool_name,
|
||
"parameters": parameters,
|
||
"message": f"调用工具: {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,
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||
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": 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,
|
||
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
|
||
}
|
||
|
||
|
||
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
|
||
|
||
【核心检索工具 - 优化后】
|
||
- insight_forge: 深度洞察检索（最强大，自动分解问题，多维度检索）
|
||
- panorama_search: 广度搜索（获取全貌，包括历史/过期内容）
|
||
- quick_search: 简单搜索（快速检索）
|
||
|
||
【重要】Report Agent必须优先调用工具获取模拟数据，而非使用自身知识！
|
||
"""
|
||
|
||
# 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
|
||
|
||
【重要】这三个工具是专门为从模拟图谱中检索信息设计的，
|
||
必须优先使用这些工具获取数据，而不是使用LLM自身的知识！
|
||
"""
|
||
return {
|
||
"insight_forge": {
|
||
"name": "insight_forge",
|
||
"description": """【深度洞察检索 - 最强大的检索工具】
|
||
这是我们最强大的检索函数，专为深度分析设计。它会：
|
||
1. 自动将你的问题分解为多个子问题
|
||
2. 从多个维度检索模拟图谱中的信息
|
||
3. 整合语义搜索、实体分析、关系链追踪的结果
|
||
4. 返回最全面、最深度的检索内容
|
||
|
||
【使用场景】
|
||
- 需要深入分析某个话题
|
||
- 需要了解事件的多个方面
|
||
- 需要获取支撑报告章节的丰富素材
|
||
|
||
【返回内容】
|
||
- 相关事实原文（可直接引用）
|
||
- 核心实体洞察
|
||
- 关系链分析""",
|
||
"parameters": {
|
||
"query": "你想深入分析的问题或话题",
|
||
"report_context": "当前报告章节的上下文（可选，有助于生成更精准的子问题）"
|
||
},
|
||
"priority": "high"
|
||
},
|
||
"panorama_search": {
|
||
"name": "panorama_search",
|
||
"description": """【广度搜索 - 获取全貌视图】
|
||
这个工具用于获取模拟结果的完整全貌，特别适合了解事件演变过程。它会：
|
||
1. 获取所有相关节点和关系
|
||
2. 区分当前有效的事实和历史/过期的事实
|
||
3. 帮助你了解舆情是如何演变的
|
||
|
||
【使用场景】
|
||
- 需要了解事件的完整发展脉络
|
||
- 需要对比不同阶段的舆情变化
|
||
- 需要获取全面的实体和关系信息
|
||
|
||
【返回内容】
|
||
- 当前有效事实（模拟最新结果）
|
||
- 历史/过期事实（演变记录）
|
||
- 所有涉及的实体""",
|
||
"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）"
|
||
},
|
||
"priority": "medium"
|
||
},
|
||
"quick_search": {
|
||
"name": "quick_search",
|
||
"description": """【简单搜索 - 快速检索】
|
||
轻量级的快速检索工具，适合简单、直接的信息查询。
|
||
|
||
【使用场景】
|
||
- 需要快速查找某个具体信息
|
||
- 需要验证某个事实
|
||
- 简单的信息检索
|
||
|
||
【返回内容】
|
||
- 与查询最相关的事实列表""",
|
||
"parameters": {
|
||
"query": "搜索查询字符串",
|
||
"limit": "返回结果数量（可选，默认10）"
|
||
},
|
||
"priority": "low"
|
||
},
|
||
"interview_agents": {
|
||
"name": "interview_agents",
|
||
"description": """【深度采访 - 真实Agent采访（双平台）】
|
||
调用OASIS模拟环境的采访API，对正在运行的模拟Agent进行真实采访！
|
||
这不是LLM模拟，而是调用真实的采访接口获取模拟Agent的原始回答。
|
||
默认在Twitter和Reddit两个平台同时采访，获取更全面的观点。
|
||
|
||
功能流程：
|
||
1. 自动读取人设文件，了解所有模拟Agent
|
||
2. 智能选择与采访主题最相关的Agent（如学生、媒体、官方等）
|
||
3. 自动生成采访问题
|
||
4. 调用 /api/simulation/interview/batch 接口在双平台进行真实采访
|
||
5. 整合所有采访结果，提供多视角分析
|
||
|
||
【使用场景】
|
||
- 需要从不同角色视角了解事件看法（学生怎么看？媒体怎么看？官方怎么说？）
|
||
- 需要收集多方意见和立场
|
||
- 需要获取模拟Agent的真实回答（来自OASIS模拟环境）
|
||
- 想让报告更生动，包含"采访实录"
|
||
|
||
【返回内容】
|
||
- 被采访Agent的身份信息
|
||
- 各Agent在Twitter和Reddit两个平台的采访回答
|
||
- 关键引言（可直接引用）
|
||
- 采访摘要和观点对比
|
||
|
||
【重要】需要OASIS模拟环境正在运行才能使用此功能！""",
|
||
"parameters": {
|
||
"interview_topic": "采访主题或需求描述（如：'了解学生对宿舍甲醛事件的看法'）",
|
||
"max_agents": "最多采访的Agent数量（可选，默认5）"
|
||
},
|
||
"priority": "high"
|
||
}
|
||
}
|
||
|
||
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:
|
||
# ========== 核心检索工具（优化后） ==========
|
||
|
||
if tool_name == "insight_forge":
|
||
# 深度洞察检索 - 最强大的工具
|
||
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", 20)
|
||
if isinstance(max_agents, str):
|
||
max_agents = int(max_agents)
|
||
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"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(f"工具执行失败: {tool_name}, 错误: {str(e)}")
|
||
return f"工具执行失败: {str(e)}"
|
||
|
||
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：
|
||
<tool_call>
|
||
{"name": "tool_name", "parameters": {"param1": "value1"}}
|
||
</tool_call>
|
||
|
||
或者：
|
||
[TOOL_CALL] tool_name(param1="value1", param2="value2")
|
||
"""
|
||
tool_calls = []
|
||
|
||
# 格式1: XML风格
|
||
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
|
||
|
||
# 格式2: 函数调用风格
|
||
func_pattern = r'\[TOOL_CALL\]\s*(\w+)\s*\((.*?)\)'
|
||
for match in re.finditer(func_pattern, response, re.DOTALL):
|
||
tool_name = match.group(1)
|
||
params_str = match.group(2)
|
||
|
||
# 解析参数
|
||
params = {}
|
||
for param_match in re.finditer(r'(\w+)\s*=\s*["\']([^"\']*)["\']', params_str):
|
||
params[param_match.group(1)] = param_match.group(2)
|
||
|
||
tool_calls.append({
|
||
"name": tool_name,
|
||
"parameters": params
|
||
})
|
||
|
||
return tool_calls
|
||
|
||
def _get_tools_description(self) -> str:
|
||
"""生成工具描述文本"""
|
||
desc_parts = ["可用工具："]
|
||
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("开始规划报告大纲...")
|
||
|
||
if progress_callback:
|
||
progress_callback("planning", 0, "正在分析模拟需求...")
|
||
|
||
# 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, "正在生成报告大纲...")
|
||
|
||
# 构建规划prompt
|
||
system_prompt = """你是一个「未来预测报告」的撰写专家，拥有对模拟世界的「上帝视角」——你可以洞察模拟中每一位Agent的行为、言论和互动。
|
||
|
||
【核心理念】
|
||
我们构建了一个模拟世界，并向其中注入了特定的「模拟需求」作为变量。模拟世界的演化结果，就是对未来可能发生情况的预测。你正在观察的不是"实验数据"，而是"未来的预演"。
|
||
|
||
【你的任务】
|
||
撰写一份「未来预测报告」，回答：
|
||
1. 在我们设定的条件下，未来发生了什么？
|
||
2. 各类Agent（人群）是如何反应和行动？
|
||
3. 这个模拟揭示了哪些值得关注的未来趋势和风险？
|
||
|
||
【报告定位】
|
||
- ✅ 这是一份基于模拟的未来预测报告，揭示"如果这样，未来会怎样"
|
||
- ✅ 聚焦于预测结果：事件走向、群体反应、涌现现象、潜在风险
|
||
- ✅ 模拟世界中的Agent言行就是对未来人群行为的预测
|
||
- ❌ 不是对现实世界现状的分析
|
||
- ❌ 不是泛泛而谈的舆情综述
|
||
|
||
【章节数量限制】
|
||
- 最少2个章节，最多5个章节
|
||
- 不需要子章节，每个章节直接撰写完整内容
|
||
- 内容要精炼，聚焦于核心预测发现
|
||
- 章节结构由你根据预测结果自主设计
|
||
|
||
请输出JSON格式的报告大纲，格式如下：
|
||
{
|
||
"title": "报告标题",
|
||
"summary": "报告摘要（一句话概括核心预测发现）",
|
||
"sections": [
|
||
{
|
||
"title": "章节标题",
|
||
"description": "章节内容描述"
|
||
}
|
||
]
|
||
}
|
||
|
||
注意：sections数组最少2个，最多5个元素！"""
|
||
|
||
user_prompt = f"""【预测场景设定】
|
||
我们向模拟世界注入的变量（模拟需求）：{self.simulation_requirement}
|
||
|
||
【模拟世界规模】
|
||
- 参与模拟的实体数量: {context.get('graph_statistics', {}).get('total_nodes', 0)}
|
||
- 实体间产生的关系数量: {context.get('graph_statistics', {}).get('total_edges', 0)}
|
||
- 实体类型分布: {list(context.get('graph_statistics', {}).get('entity_types', {}).keys())}
|
||
- 活跃Agent数量: {context.get('total_entities', 0)}
|
||
|
||
【模拟预测到的部分未来事实样本】
|
||
{json.dumps(context.get('related_facts', [])[:10], ensure_ascii=False, indent=2)}
|
||
|
||
请以「上帝视角」审视这个未来预演：
|
||
1. 在我们设定的条件下，未来呈现出了什么样的状态？
|
||
2. 各类人群（Agent）是如何反应和行动的？
|
||
3. 这个模拟揭示了哪些值得关注的未来趋势？
|
||
|
||
根据预测结果，设计最合适的报告章节结构。
|
||
|
||
【再次提醒】报告章节数量：最少2个，最多5个，内容要精炼聚焦于核心预测发现。"""
|
||
|
||
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, "正在解析大纲结构...")
|
||
|
||
# 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", "模拟分析报告"),
|
||
summary=response.get("summary", ""),
|
||
sections=sections
|
||
)
|
||
|
||
if progress_callback:
|
||
progress_callback("planning", 100, "大纲规划完成")
|
||
|
||
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="未来预测报告",
|
||
summary="基于模拟预测的未来趋势与风险分析",
|
||
sections=[
|
||
ReportSection(title="预测场景与核心发现"),
|
||
ReportSection(title="人群行为预测分析"),
|
||
ReportSection(title="趋势展望与风险提示")
|
||
]
|
||
)
|
||
|
||
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)
|
||
|
||
# 构建系统prompt - 优化后强调工具使用和引用原文
|
||
# 确定当前章节的标题级别
|
||
section_level = 2 # 默认为二级标题（##）
|
||
sub_heading_level = 3 # 子标题使用三级（###）
|
||
sub_sub_heading_level = 4 # 更小的子标题使用四级（####）
|
||
|
||
system_prompt = f"""你是一个「未来预测报告」的撰写专家，正在撰写报告的一个章节。
|
||
|
||
报告标题: {outline.title}
|
||
报告摘要: {outline.summary}
|
||
预测场景（模拟需求）: {self.simulation_requirement}
|
||
|
||
当前要撰写的章节: {section.title}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【核心理念】
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
模拟世界是对未来的预演。我们向模拟世界注入了特定条件（模拟需求），
|
||
模拟中Agent的行为和互动，就是对未来人群行为的预测。
|
||
|
||
你的任务是：
|
||
- 揭示在设定条件下，未来发生了什么
|
||
- 预测各类人群（Agent）是如何反应和行动的
|
||
- 发现值得关注的未来趋势、风险和机会
|
||
|
||
❌ 不要写成对现实世界现状的分析
|
||
✅ 要聚焦于"未来会怎样"——模拟结果就是预测的未来
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【最重要的规则 - 必须遵守】
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
1. 【必须调用工具观察模拟世界】
|
||
- 你正在以「上帝视角」观察未来的预演
|
||
- 所有内容必须来自模拟世界中发生的事件和Agent言行
|
||
- 禁止使用你自己的知识来编写报告内容
|
||
- 每个章节至少调用2次工具（最多4次）来观察模拟的世界，它代表了未来
|
||
|
||
2. 【必须引用Agent的原始言行】
|
||
- Agent的发言和行为是对未来人群行为的预测
|
||
- 在报告中使用引用格式展示这些预测，例如：
|
||
> "某类人群会表示：原文内容..."
|
||
- 这些引用是模拟预测的核心证据
|
||
|
||
3. 【忠实呈现预测结果】
|
||
- 报告内容必须反映模拟世界中的代表未来的模拟结果
|
||
- 不要添加模拟中不存在的信息
|
||
- 如果某方面信息不足，如实说明
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【⚠️ 格式规范 - 极其重要！】
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
【一个章节 = 最小内容单位】
|
||
- 每个章节是报告的最小分块单位
|
||
- ❌ 禁止在章节内使用任何 Markdown 标题（#、##、###、#### 等）
|
||
- ❌ 禁止在内容开头添加章节主标题
|
||
- ✅ 章节标题由系统自动添加，你只需撰写纯正文内容
|
||
- ✅ 使用**粗体**、段落分隔、引用、列表来组织内容，但不要用标题
|
||
|
||
【正确示例】
|
||
```
|
||
本章节分析了事件的舆论传播态势。通过对模拟数据的深入分析，我们发现...
|
||
|
||
**首发引爆阶段**
|
||
|
||
微博作为舆情的第一现场，承担了信息首发的核心功能：
|
||
|
||
> "微博贡献了68%的首发声量..."
|
||
|
||
**情绪放大阶段**
|
||
|
||
抖音平台进一步放大了事件影响力：
|
||
|
||
- 视觉冲击力强
|
||
- 情绪共鸣度高
|
||
```
|
||
|
||
【错误示例】
|
||
```
|
||
## 执行摘要 ← 错误！不要添加任何标题
|
||
### 一、首发阶段 ← 错误！不要用###分小节
|
||
#### 1.1 详细分析 ← 错误！不要用####细分
|
||
|
||
本章节分析了...
|
||
```
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【可用检索工具】（每章节调用2-4次）
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
{self._get_tools_description()}
|
||
|
||
【工具使用建议】
|
||
- insight_forge: 用于深度分析，会自动分解问题并多维度检索
|
||
- panorama_search: 用于了解全貌和演变过程
|
||
- quick_search: 用于快速验证某个具体信息
|
||
- interview_agents: 用于采访模拟Agent，获取不同角色的真实观点和看法
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【ReACT工作流程】
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
1. Thought: [分析需要什么信息，规划检索策略]
|
||
2. Action: [调用工具获取信息]
|
||
<tool_call>
|
||
{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
|
||
</tool_call>
|
||
3. Observation: [分析工具返回的结果]
|
||
4. 重复步骤1-3，直到收集到足够信息（最多5轮）
|
||
5. Final Answer: [基于检索结果撰写章节内容]
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【章节内容要求】
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
1. 内容必须基于工具检索到的模拟数据
|
||
2. 大量引用原文来展示模拟效果
|
||
3. 使用Markdown格式（但禁止使用标题）：
|
||
- 使用 **粗体文字** 标记重点（代替子标题）
|
||
- 使用列表（-或1.2.3.）组织要点
|
||
- 使用空行分隔不同段落
|
||
- ❌ 禁止使用 #、##、###、#### 等任何标题语法
|
||
4. 【引用格式规范 - 必须单独成段】
|
||
引用必须独立成段，前后各有一个空行，不能混在段落中：
|
||
|
||
✅ 正确格式：
|
||
```
|
||
校方的回应被认为缺乏实质内容。
|
||
|
||
> "校方的应对模式在瞬息万变的社交媒体环境中显得僵化和迟缓。"
|
||
|
||
这一评价反映了公众的普遍不满。
|
||
```
|
||
|
||
❌ 错误格式：
|
||
```
|
||
校方的回应被认为缺乏实质内容。> "校方的应对模式..." 这一评价反映了...
|
||
```
|
||
5. 保持与其他章节的逻辑连贯性
|
||
6. 【避免重复】仔细阅读下方已完成的章节内容，不要重复描述相同的信息
|
||
7. 【再次强调】不要添加任何标题！用**粗体**代替小节标题"""
|
||
|
||
# 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 = f"""已完成的章节内容（请仔细阅读，避免重复）：
|
||
{previous_content}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【当前任务】撰写章节: {section.title}
|
||
═══════════════════════════════════════════════════════════════
|
||
|
||
【重要提醒】
|
||
1. 仔细阅读上方已完成的章节，避免重复相同的内容！
|
||
2. 开始前必须先调用工具获取模拟数据
|
||
3. 推荐先使用 insight_forge 进行深度检索
|
||
4. 报告内容必须来自检索结果，不要使用自己的知识
|
||
|
||
【⚠️ 格式警告 - 必须遵守】
|
||
- ❌ 不要写任何标题（#、##、###、####都不行）
|
||
- ❌ 不要写"{section.title}"作为开头
|
||
- ✅ 章节标题由系统自动添加
|
||
- ✅ 直接写正文，用**粗体**代替小节标题
|
||
|
||
请开始：
|
||
1. 首先思考（Thought）这个章节需要什么信息
|
||
2. 然后调用工具（Action）获取模拟数据
|
||
3. 收集足够信息后输出 Final Answer（纯正文，无任何标题）"""
|
||
|
||
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 = 2 # 最少工具调用次数
|
||
|
||
# 报告上下文，用于InsightForge的子问题生成
|
||
report_context = f"章节标题: {section.title}\n模拟需求: {self.simulation_requirement}"
|
||
|
||
for iteration in range(max_iterations):
|
||
if progress_callback:
|
||
progress_callback(
|
||
"generating",
|
||
int((iteration / max_iterations) * 100),
|
||
f"深度检索与撰写中 ({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": "（响应为空）"})
|
||
messages.append({"role": "user", "content": "请继续生成内容。"})
|
||
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]}...")
|
||
|
||
# 检查是否有工具调用和最终答案
|
||
has_tool_calls = bool(self._parse_tool_calls(response))
|
||
has_final_answer = "Final Answer:" in response
|
||
|
||
# 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
|
||
)
|
||
|
||
# 检查是否有最终答案
|
||
if has_final_answer:
|
||
# 如果工具调用次数不足，提醒需要更多检索
|
||
if tool_calls_count < min_tool_calls:
|
||
messages.append({"role": "assistant", "content": response})
|
||
messages.append({
|
||
"role": "user",
|
||
"content": f"""【注意】你只调用了{tool_calls_count}次工具，信息可能不够充分。
|
||
|
||
请再调用1-2次工具来获取更多模拟数据，然后再输出 Final Answer。
|
||
建议：
|
||
- 使用 insight_forge 深度检索更多细节
|
||
- 使用 panorama_search 了解事件全貌
|
||
|
||
记住：报告内容必须来自模拟结果，而不是你的知识！"""
|
||
})
|
||
continue
|
||
|
||
# 提取最终答案
|
||
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）")
|
||
|
||
# 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
|
||
|
||
# 解析工具调用
|
||
tool_calls = self._parse_tool_calls(response)
|
||
|
||
if not tool_calls:
|
||
# 没有工具调用也没有最终答案
|
||
messages.append({"role": "assistant", "content": response})
|
||
|
||
if tool_calls_count < min_tool_calls:
|
||
# 还没有足够的工具调用，强烈提示需要调用工具
|
||
messages.append({
|
||
"role": "user",
|
||
"content": f"""【重要】你还没有调用足够的工具来获取模拟数据！
|
||
|
||
当前只调用了 {tool_calls_count} 次工具，至少需要 {min_tool_calls} 次。
|
||
|
||
请立即调用工具获取信息：
|
||
<tool_call>
|
||
{{"name": "insight_forge", "parameters": {{"query": "{section.title}相关的模拟结果和分析"}}}}
|
||
</tool_call>
|
||
|
||
【记住】报告内容必须100%来自模拟结果，不能使用你自己的知识！"""
|
||
})
|
||
else:
|
||
# 已有足够调用，可以生成最终答案
|
||
messages.append({
|
||
"role": "user",
|
||
"content": "你已经获取了足够的模拟数据。请基于检索到的信息，输出 Final Answer: 并撰写章节内容。\n\n【重要】内容必须大量引用检索到的原文，使用 > 格式引用。"
|
||
})
|
||
continue
|
||
|
||
# 执行工具调用
|
||
tool_results = []
|
||
for call in tool_calls:
|
||
if tool_calls_count >= self.MAX_TOOL_CALLS_PER_SECTION:
|
||
break
|
||
|
||
# 记录工具调用日志
|
||
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
|
||
)
|
||
|
||
# 记录工具结果日志
|
||
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_results.append(f"═══ 工具 {call['name']} 返回 ═══\n{result}")
|
||
tool_calls_count += 1
|
||
|
||
# 将结果添加到消息
|
||
messages.append({"role": "assistant", "content": response})
|
||
messages.append({
|
||
"role": "user",
|
||
"content": f"""Observation（检索结果）:
|
||
|
||
{"".join(tool_results)}
|
||
|
||
═══════════════════════════════════════════════════════════════
|
||
【下一步行动】
|
||
- 如果信息充分：输出 Final Answer 并撰写章节内容（必须引用上述原文）
|
||
- 如果需要更多信息：继续调用工具检索
|
||
|
||
已调用工具 {tool_calls_count}/{self.MAX_TOOL_CALLS_PER_SECTION} 次
|
||
═══════════════════════════════════════════════════════════════"""
|
||
})
|
||
|
||
# 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": "已达到工具调用限制，请直接输出 Final Answer: 并生成章节内容。"
|
||
})
|
||
|
||
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"（本章节生成失败：LLM 返回空响应，请稍后重试）"
|
||
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, "初始化报告...",
|
||
completed_sections=[]
|
||
)
|
||
ReportManager.save_report(report)
|
||
|
||
# 阶段1: 规划大纲
|
||
report.status = ReportStatus.PLANNING
|
||
ReportManager.update_progress(
|
||
report_id, "planning", 5, "开始规划报告大纲...",
|
||
completed_sections=[]
|
||
)
|
||
|
||
# 记录规划开始日志
|
||
self.report_logger.log_planning_start()
|
||
|
||
if progress_callback:
|
||
progress_callback("planning", 0, "开始规划报告大纲...")
|
||
|
||
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"大纲规划完成，共{len(outline.sections)}个章节",
|
||
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"正在生成章节: {section.title} ({section_num}/{total_sections})",
|
||
current_section=section.title,
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
if progress_callback:
|
||
progress_callback(
|
||
"generating",
|
||
base_progress,
|
||
f"正在生成章节: {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"章节 {section.title} 已完成",
|
||
current_section=None,
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
# 阶段3: 组装完整报告
|
||
if progress_callback:
|
||
progress_callback("generating", 95, "正在组装完整报告...")
|
||
|
||
ReportManager.update_progress(
|
||
report_id, "generating", 95, "正在组装完整报告...",
|
||
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, "报告生成完成",
|
||
completed_sections=completed_section_titles
|
||
)
|
||
|
||
if progress_callback:
|
||
progress_callback("completed", 100, "报告生成完成")
|
||
|
||
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"报告生成失败: {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... [报告内容已截断] ..."
|
||
except Exception as e:
|
||
logger.warning(f"获取报告内容失败: {e}")
|
||
|
||
# 构建系统提示
|
||
system_prompt = f"""你是一个简洁高效的模拟预测助手。
|
||
|
||
【背景】
|
||
预测条件: {self.simulation_requirement}
|
||
|
||
【已生成的分析报告】
|
||
{report_content if report_content else "（暂无报告）"}
|
||
|
||
【规则】
|
||
1. 优先基于上述报告内容回答问题
|
||
2. 直接回答问题，避免冗长的思考论述
|
||
3. 仅在报告内容不足以回答时，才调用工具检索更多数据
|
||
4. 回答要简洁、清晰、有条理
|
||
|
||
【可用工具】（仅在需要时使用，最多调用1-2次）
|
||
{self._get_tools_description()}
|
||
|
||
【工具调用格式】
|
||
<tool_call>
|
||
{{"name": "工具名称", "parameters": {{"参数名": "参数值"}}}}
|
||
</tool_call>
|
||
|
||
【回答风格】
|
||
- 简洁直接，不要长篇大论
|
||
- 使用 > 格式引用关键内容
|
||
- 优先给出结论，再解释原因"""
|
||
|
||
# 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 + "\n\n请简洁回答问题。"
|
||
})
|
||
|
||
# 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
|