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MicroFish/backend/app/services/report_agent.py

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"""
Report Agent服务
使用LangChain + Zep实现ReACT模式的模拟报告生成
功能：
1. 根据模拟需求和Zep图谱信息生成报告
2. 先规划目录结构，然后分段生成
3. 每段采用ReACT多轮思考与反思模式
4. 支持与用户对话，在对话中自主调用检索工具
"""
import os
import json
import time
import re
from typing import Dict, Any, List, Optional, Callable
from dataclasses import dataclass, field
from datetime import datetime
from enum import Enum
from ..config import Config
from ..utils.llm_client import LLMClient
from ..utils.logger import get_logger
from ..utils.locale import get_language_instruction, t
from .zep_tools import (
ZepToolsService,
SearchResult,
InsightForgeResult,
PanoramaResult,
InterviewResult
)
logger = get_logger('mirofish.report_agent')
class ReportLogger:
"""
Report Agent 详细日志记录器
在报告文件夹中生成 agent_log.jsonl 文件，记录每一步详细动作。
每行是一个完整的 JSON 对象，包含时间戳、动作类型、详细内容等。
"""
def __init__(self, report_id: str):
"""
初始化日志记录器
Args:
report_id: 报告ID，用于确定日志文件路径
"""
self.report_id = report_id
self.log_file_path = os.path.join(
Config.UPLOAD_FOLDER, 'reports', report_id, 'agent_log.jsonl'
)
self.start_time = datetime.now()
self._ensure_log_file()
def _ensure_log_file(self):
"""确保日志文件所在目录存在"""
log_dir = os.path.dirname(self.log_file_path)
os.makedirs(log_dir, exist_ok=True)
def _get_elapsed_time(self) -> float:
"""获取从开始到现在的耗时（秒）"""
return (datetime.now() - self.start_time).total_seconds()
def log(
self,
action: str,
stage: str,
details: Dict[str, Any],
section_title: str = None,
section_index: int = None
):
"""
记录一条日志
Args:
action: 动作类型，如 'start', 'tool_call', 'llm_response', 'section_complete' 等
stage: 当前阶段，如 'planning', 'generating', 'completed'
details: 详细内容字典，不截断
section_title: 当前章节标题（可选）
section_index: 当前章节索引（可选）
"""
log_entry = {
"timestamp": datetime.now().isoformat(),
"elapsed_seconds": round(self._get_elapsed_time(), 2),
"report_id": self.report_id,
"action": action,
"stage": stage,
"section_title": section_title,
"section_index": section_index,
"details": details
}
# 追加写入 JSONL 文件
with open(self.log_file_path, 'a', encoding='utf-8') as f:
f.write(json.dumps(log_entry, ensure_ascii=False) + '\n')
def log_start(self, simulation_id: str, graph_id: str, simulation_requirement: str):
"""记录报告生成开始"""
self.log(
action="report_start",
stage="pending",
details={
"simulation_id": simulation_id,
"graph_id": graph_id,
"simulation_requirement": simulation_requirement,
"message": t('report.taskStarted')
}
)
def log_planning_start(self):
"""记录大纲规划开始"""
self.log(
action="planning_start",
stage="planning",
details={"message": t('report.planningStart')}
)
def log_planning_context(self, context: Dict[str, Any]):
"""记录规划时获取的上下文信息"""
self.log(
action="planning_context",
stage="planning",
details={
"message": t('report.fetchSimContext'),
"context": context
}
)
def log_planning_complete(self, outline_dict: Dict[str, Any]):
"""记录大纲规划完成"""
self.log(
action="planning_complete",
stage="planning",
details={
"message": t('report.planningComplete'),
"outline": outline_dict
}
)
def log_section_start(self, section_title: str, section_index: int):
"""记录章节生成开始"""
self.log(
action="section_start",
stage="generating",
section_title=section_title,
section_index=section_index,
details={"message": t('report.sectionStart', title=section_title)}
)
def log_react_thought(self, section_title: str, section_index: int, iteration: int, thought: str):
"""记录 ReACT 思考过程"""
self.log(
action="react_thought",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"iteration": iteration,
"thought": thought,
"message": t('report.reactThought', iteration=iteration)
}
)
def log_tool_call(
self,
section_title: str,
section_index: int,
tool_name: str,
parameters: Dict[str, Any],
iteration: int
):
"""记录工具调用"""
self.log(
action="tool_call",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"iteration": iteration,
"tool_name": tool_name,
"parameters": parameters,
"message": t('report.toolCall', toolName=tool_name)
}
)
def log_tool_result(
self,
section_title: str,
section_index: int,
tool_name: str,
result: str,
iteration: int
):
"""记录工具调用结果（完整内容，不截断）"""
self.log(
action="tool_result",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"iteration": iteration,
"tool_name": tool_name,
"result": result, # 完整结果，不截断
"result_length": len(result),
"message": t('report.toolResult', toolName=tool_name)
}
)
def log_llm_response(
self,
section_title: str,
section_index: int,
response: str,
iteration: int,
has_tool_calls: bool,
has_final_answer: bool
):
"""记录 LLM 响应（完整内容，不截断）"""
self.log(
action="llm_response",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"iteration": iteration,
"response": response, # 完整响应，不截断
"response_length": len(response),
"has_tool_calls": has_tool_calls,
"has_final_answer": has_final_answer,
"message": t('report.llmResponse', hasToolCalls=has_tool_calls, hasFinalAnswer=has_final_answer)
}
)
def log_section_content(
self,
section_title: str,
section_index: int,
content: str,
tool_calls_count: int
):
"""记录章节内容生成完成（仅记录内容，不代表整个章节完成）"""
self.log(
action="section_content",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"content": content, # 完整内容，不截断
"content_length": len(content),
"tool_calls_count": tool_calls_count,
"message": t('report.sectionContentDone', title=section_title)
}
)
def log_section_full_complete(
self,
section_title: str,
section_index: int,
full_content: str
):
"""
记录章节生成完成
前端应监听此日志来判断一个章节是否真正完成，并获取完整内容
"""
self.log(
action="section_complete",
stage="generating",
section_title=section_title,
section_index=section_index,
details={
"content": full_content,
"content_length": len(full_content),
"message": t('report.sectionComplete', title=section_title)
}
)
def log_report_complete(self, total_sections: int, total_time_seconds: float):
"""记录报告生成完成"""
self.log(
action="report_complete",
stage="completed",
details={
"total_sections": total_sections,
"total_time_seconds": round(total_time_seconds, 2),
"message": t('report.reportComplete')
}
)
def log_error(self, error_message: str, stage: str, section_title: str = None):
"""记录错误"""
self.log(
action="error",
stage=stage,
section_title=section_title,
section_index=None,
details={
"error": error_message,
"message": t('report.errorOccurred', error=error_message)
}
)
class ReportConsoleLogger:
"""
Report Agent 控制台日志记录器
将控制台风格的日志（INFO、WARNING等）写入报告文件夹中的 console_log.txt 文件。
这些日志与 agent_log.jsonl 不同，是纯文本格式的控制台输出。
"""
def __init__(self, report_id: str):
"""
初始化控制台日志记录器
Args:
report_id: 报告ID，用于确定日志文件路径
"""
self.report_id = report_id
self.log_file_path = os.path.join(
Config.UPLOAD_FOLDER, 'reports', report_id, 'console_log.txt'
)
self._ensure_log_file()
self._file_handler = None
self._setup_file_handler()
def _ensure_log_file(self):
"""确保日志文件所在目录存在"""
log_dir = os.path.dirname(self.log_file_path)
os.makedirs(log_dir, exist_ok=True)
def _setup_file_handler(self):
"""设置文件处理器，将日志同时写入文件"""
import logging
# 创建文件处理器
self._file_handler = logging.FileHandler(
self.log_file_path,
mode='a',
encoding='utf-8'
)
self._file_handler.setLevel(logging.INFO)
# 使用与控制台相同的简洁格式
formatter = logging.Formatter(
'[%(asctime)s] %(levelname)s: %(message)s',
datefmt='%H:%M:%S'
)
self._file_handler.setFormatter(formatter)
# 添加到 report_agent 相关的 logger
loggers_to_attach = [
'mirofish.report_agent',
'mirofish.zep_tools',
]
for logger_name in loggers_to_attach:
target_logger = logging.getLogger(logger_name)
# 避免重复添加
if self._file_handler not in target_logger.handlers:
target_logger.addHandler(self._file_handler)
def close(self):
"""关闭文件处理器并从 logger 中移除"""
import logging
if self._file_handler:
loggers_to_detach = [
'mirofish.report_agent',
'mirofish.zep_tools',
]
for logger_name in loggers_to_detach:
target_logger = logging.getLogger(logger_name)
if self._file_handler in target_logger.handlers:
target_logger.removeHandler(self._file_handler)
self._file_handler.close()
self._file_handler = None
def __del__(self):
"""析构时确保关闭文件处理器"""
self.close()
class ReportStatus(str, Enum):
"""报告状态"""
PENDING = "pending"
PLANNING = "planning"
GENERATING = "generating"
COMPLETED = "completed"
FAILED = "failed"
@dataclass
class ReportSection:
"""报告章节"""
title: str
content: str = ""
def to_dict(self) -> Dict[str, Any]:
return {
"title": self.title,
"content": self.content
}
def to_markdown(self, level: int = 2) -> str:
"""转换为Markdown格式"""
md = f"{'#' * level} {self.title}\n\n"
if self.content:
md += f"{self.content}\n\n"
return md
@dataclass
class ReportOutline:
"""报告大纲"""
title: str
summary: str
sections: List[ReportSection]
def to_dict(self) -> Dict[str, Any]:
return {
"title": self.title,
"summary": self.summary,
"sections": [s.to_dict() for s in self.sections]
}
def to_markdown(self) -> str:
"""转换为Markdown格式"""
md = f"# {self.title}\n\n"
md += f"> {self.summary}\n\n"
for section in self.sections:
md += section.to_markdown()
return md
@dataclass
class Report:
"""完整报告"""
report_id: str
simulation_id: str
graph_id: str
simulation_requirement: str
status: ReportStatus
outline: Optional[ReportOutline] = None
markdown_content: str = ""
created_at: str = ""
completed_at: str = ""
error: Optional[str] = None
def to_dict(self) -> Dict[str, Any]:
return {
"report_id": self.report_id,
"simulation_id": self.simulation_id,
"graph_id": self.graph_id,
"simulation_requirement": self.simulation_requirement,
"status": self.status.value,
"outline": self.outline.to_dict() if self.outline else None,
"markdown_content": self.markdown_content,
"created_at": self.created_at,
"completed_at": self.completed_at,
"error": self.error
}
# ═══════════════════════════════════════════════════════════════
# Prompt 模板常量
# ═══════════════════════════════════════════════════════════════
# ── 工具描述 ──
TOOL_DESC_INSIGHT_FORGE = """\
[Deep Insight Retrieval — Powerful Analytical Tool]
This is our most 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 along multiple dimensions.
3. Integrate semantic search, entity analysis, and relationship-chain tracing.
4. Return the most comprehensive and in-depth retrieval content.
[When to use]
- You need an in-depth analysis of a topic.
- You need to understand multiple facets of an event.
- You need rich source material to support a report section.
[Return content]
- Relevant factual quotes (ready to cite verbatim).
- Core entity insights.
- Relationship-chain analysis."""
TOOL_DESC_PANORAMA_SEARCH = """\
[Panorama Search — Full-View Retrieval]
This tool retrieves a complete panorama of the simulation result, ideal for understanding how an event evolved. It will:
1. Pull every related node and relationship.
2. Distinguish currently valid facts from historical or expired facts.
3. Help you trace how public opinion evolved over time.
[When to use]
- You need the full timeline of an event.
- You need to compare opinion shifts between different stages.
- You need a comprehensive view of all entities and relationships.
[Return content]
- Currently valid facts (the latest simulation state).
- Historical or expired facts (the evolution record).
- Every entity involved."""
TOOL_DESC_QUICK_SEARCH = """\
[Quick Search — Lightweight Retrieval]
A lightweight retrieval tool, best for simple, direct lookups.
[When to use]
- You need a quick lookup for a specific piece of information.
- You need to verify a single fact.
- Simple information retrieval.
[Return content]
- A 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 a real interview against the running simulation agents.
This is NOT an LLM simulation — it invokes the real interview endpoint and returns the simulated agents' raw answers.
By default it interviews on both Twitter and Reddit in parallel, capturing more diverse viewpoints.
How it works:
1. Reads the persona files automatically to learn about every simulated agent.
2. Selects the agents most relevant to the interview topic (students, media, officials, etc.).
3. Generates the interview questions automatically.
4. Calls the /api/simulation/interview/batch endpoint on both platforms.
5. Integrates all interview results to provide a multi-perspective view.
[When to use]
- You need to understand an event from different role perspectives (what do students think? What does the media say? What is the official line?).
- You need to collect multi-party opinions and stances.
- You need real answers from simulated agents (sourced from the OASIS simulation environment).
- You want the report to feel vivid and include first-hand "interview transcripts".
[Return content]
- The interviewee agent's identity information.
- Each agent's interview answers on both Twitter and Reddit.
- Key quotations (ready to cite verbatim).
- Interview summary and viewpoint comparison.
[IMPORTANT] A running OASIS simulation environment is required to use this tool!"""
# ── 大纲规划 prompt ──
PLAN_SYSTEM_PROMPT = """\
You are an expert author of "Future Prediction Reports" with a god's-eye view of the simulated world — you can observe the behavior, statements, and interactions of every agent in the simulation.
[Core idea]
We have built a simulated world and injected a specific "simulation requirement" into it as the input variable. The way that simulated world evolves is itself a prediction of what could happen in reality. You are not looking at "experimental data" — you are watching a rehearsal of the future.
[Your task]
Author a "Future Prediction Report" that answers:
1. Under the conditions we configured, what happened in the future?
2. How did the various agent groups (populations) react and behave?
3. What noteworthy future trends and risks does this simulation reveal?
[Report framing]
- ✅ This is a prediction report grounded in a simulation; it reveals "if X, then what does the future look like".
- ✅ Focus on the predicted outcomes: how the event evolves, group reactions, emergent phenomena, latent risks.
- ✅ Treat the simulated agents' statements and behavior as the prediction of how real-world populations would behave.
- ❌ This is NOT an analysis of the present-day world.
- ❌ This is NOT a generic public-opinion summary.
[Section-count limits]
- Minimum 2 sections, maximum 5 sections.
- No sub-sections — each section is written as a single block of content.
- Keep the content focused; concentrate on the core prediction findings.
- You design the section structure freely based on the prediction outcomes.
Return the report outline as JSON in the following shape:
{
"title": "Report title",
"summary": "Report summary (a one-sentence distillation of the core prediction findings)",
"sections": [
{
"title": "Section title",
"description": "Section content description"
}
]
}
Note: the `sections` array MUST contain at least 2 and at most 5 elements!"""
PLAN_USER_PROMPT_TEMPLATE = """\
[Prediction scenario]
The variable we injected into the simulated world (simulation requirement): {simulation_requirement}
[Simulation scale]
- Total entities participating in the simulation: {total_nodes}
- Total relationships generated between entities: {total_edges}
- Entity-type distribution: {entity_types}
- Active agent count: {total_entities}
[Sample of predicted future facts from the simulation]
{related_facts_json}
Take the god's-eye view of this future rehearsal:
1. Under the conditions we configured, what state does the future reveal?
2. How did the various populations (agents) react and behave?
3. What noteworthy future trends does this simulation reveal?
Based on these prediction outcomes, design the most appropriate section structure for the report.
[Reminder] Section count: minimum 2, maximum 5; keep the content tight and focused on the core prediction findings."""
# ── 章节生成 prompt ──
SECTION_SYSTEM_PROMPT_TEMPLATE = """\
You are an expert author of "Future Prediction Reports" and you are currently writing one section of the report.
Report title: {report_title}
Report summary: {report_summary}
Prediction scenario (simulation requirement): {simulation_requirement}
Section to write right now: {section_title}
═══════════════════════════════════════════════════════════════
[Core idea]
═══════════════════════════════════════════════════════════════
The simulated world is a rehearsal of the future. We injected specific conditions (the simulation requirement) into it,
and the agents' behavior and interactions in that simulation are themselves a prediction of how real populations would behave.
Your task is to:
- Reveal what happened in the future under the configured conditions.
- Predict how each population (agent group) reacted and behaved.
- Surface the noteworthy future trends, risks, and opportunities.
❌ Do not write a present-day analysis of the real world.
✅ Stay focused on "what does the future look like" — the simulation outcome IS the predicted future.
═══════════════════════════════════════════════════════════════
[Most important rules — MUST follow]
═══════════════════════════════════════════════════════════════
1. [You MUST call tools to observe the simulated world]
- You are watching the future rehearsal from a god's-eye view.
- All content MUST come from events and agent statements/behavior in the simulated world.
- Do NOT use your own prior knowledge to author report content.
- Each section MUST call retrieval tools at least 3 times (and at most 5 times) to observe the simulated world, which represents the future.
2. [You MUST quote agents' raw statements and behavior]
- The agents' speech and actions ARE the prediction of how real populations would behave.
- Render these predictions in the report using block-quote format, for example:
> "A specific population would say: <verbatim quote>..."
- These quotations are the core evidence of the simulation's prediction.
3. [Language consistency — translate quoted material into the report language]
- Tool results may contain text in a language that differs from the report language.
- The report MUST be authored entirely in the language requested by the user.
- When you quote tool output that is in a different language, translate it into the report language before writing it in.
- Preserve the original meaning during translation; the rendered text must read naturally.
- This rule applies both to body text and to block-quote (>) content.
4. [Faithfully render the prediction outcomes]
- The report content MUST reflect the simulated outcomes that represent the future.
- Do NOT add information that does not exist in the simulation.
- If the simulation lacks coverage of an aspect, say so honestly.
═══════════════════════════════════════════════════════════════
[⚠️ Formatting rules — extremely important!]
═══════════════════════════════════════════════════════════════
[One section = the smallest unit of content]
- Each section is the smallest content block in the report.
- ❌ Do NOT use any Markdown heading (#, ##, ###, ####, etc.) inside the section.
- ❌ Do NOT prepend the section's main heading at the start of the content.
- ✅ The section title is added by the system automatically — you write only the body content.
- ✅ Use **bold**, paragraph breaks, block quotes, and lists to organize the content — but no headings.
[Correct example]
```
This section analyzes how public opinion propagated around the event. A close reading of the simulated data reveals that...
**Initial-spark stage**
Platform A served as the first venue for the news, fulfilling its core role as a launcher of viral information:
> "Platform A produced 68% of the first-wave volume..."
**Emotional-amplification stage**
Platform B further amplified the event's reach:
- Strong visual impact
- High emotional resonance
```
[Wrong example]
```
## Executive Summary ← Wrong! Do not add any heading.
### 1. Initial-spark stage ← Wrong! Do not use ### for sub-sections.
#### 1.1 Detailed analysis ← Wrong! Do not use #### either.
This section analyzes...
```
═══════════════════════════════════════════════════════════════
[Available retrieval tools] (3–5 calls per section)
═══════════════════════════════════════════════════════════════
{tools_description}
[Tool-usage guidance — mix different tools; do not rely on just one]
- insight_forge: deep analytical retrieval; auto-decomposes the question and pulls facts and relationships from multiple angles.
- panorama_search: wide-angle panoramic search; reveals the full picture of an event, its timeline, and how it evolved.
- quick_search: quick verification of a specific information point.
- interview_agents: interview the simulated agents to capture first-person viewpoints and authentic reactions across roles.
═══════════════════════════════════════════════════════════════
[Workflow]
═══════════════════════════════════════════════════════════════
Each reply may do exactly ONE of the following two things (never both):
Option A — Call a tool:
Write your reasoning, then invoke one tool using the format below:
<tool_call>
{{"name": "<tool name>", "parameters": {{"<param name>": "<param value>"}}}}
</tool_call>
The system will run the tool and return its result. You do NOT need to (and MUST not) author the tool result yourself.
Option B — Output the final content:
Once you have gathered enough information through tool calls, output the section content prefixed with "Final Answer:".
⚠️ Strictly forbidden:
- Do NOT include both a tool call and a Final Answer in the same reply.
- Do NOT fabricate tool results (Observation) yourself; all tool results are injected by the system.
- Each reply may invoke at most one tool.
═══════════════════════════════════════════════════════════════
[Section-content requirements]
═══════════════════════════════════════════════════════════════
1. The content MUST be grounded in the simulated data retrieved by the tools.
2. Quote source material liberally to make the simulation's predictions vivid.
3. Use Markdown formatting (but no headings):
- Use **bold** to emphasize key points (instead of sub-headings).
- Use lists (- or 1./2./3.) to organize bullet points.
- Separate paragraphs with blank lines.
- ❌ Do NOT use #, ##, ###, #### — no heading syntax of any kind.
4. [Quotation format — must stand alone as its own paragraph]
A block quote MUST be its own paragraph, with a blank line above and below; do not embed it inside another paragraph:
✅ Correct format:
```
The university's response was widely viewed as substanceless.
> "The university's response pattern reads as rigid and slow in a fast-moving social-media environment."
This assessment captures the public's broad dissatisfaction.
```
❌ Wrong format:
```
The university's response was widely viewed as substanceless. > "The university's response pattern..." This assessment captures...
```
5. Maintain logical continuity with the other sections.
6. [Avoid repetition] Read the already-completed section content below carefully and do not repeat the same information.
7. [Reminder] Do NOT add any headings! Use **bold** instead of sub-section titles."""
SECTION_USER_PROMPT_TEMPLATE = """\
Already-completed section content (read carefully to avoid repeating yourself):
{previous_content}
═══════════════════════════════════════════════════════════════
[Current task] Write section: {section_title}
═══════════════════════════════════════════════════════════════
[Important reminders]
1. Read the already-completed sections above carefully and avoid repeating the same content.
2. You MUST call a retrieval tool first to obtain simulated data before writing.
3. Mix different tools — do not rely on a single one.
4. The report content MUST come from the retrieval results; do not use your own prior knowledge.
[⚠️ Formatting warning — MUST follow]
- ❌ Do NOT write any heading (no #, ##, ###, or ####).
- ❌ Do NOT write "{section_title}" as the opening line.
- ✅ The section title is added by the system automatically.
- ✅ Write the body directly; use **bold** instead of sub-section titles.
Get started:
1. First think (Thought) about what information this section needs.
2. Then call a tool (Action) to retrieve the simulated data.
3. Once you have gathered enough information, output the body prefixed with Final Answer: (plain body, no headings)."""
# ── ReACT 循环内消息模板 ──
REACT_OBSERVATION_TEMPLATE = """\
Observation (retrieval result):
═══ Tool {tool_name} returned ═══
{result}
═══════════════════════════════════════════════════════════════
Tool calls so far: {tool_calls_count}/{max_tool_calls} (used: {used_tools_str}){unused_hint}
- If you have enough information: output the section content prefixed with "Final Answer:" (you MUST quote the source material above).
- If you need more information: call one more tool to continue retrieving.
═══════════════════════════════════════════════════════════════"""
REACT_INSUFFICIENT_TOOLS_MSG = (
"[Note] You have only called tools {tool_calls_count} times; at least {min_tool_calls} are required. "
"Call more tools to gather simulation data, then output Final Answer.{unused_hint}"
)
REACT_INSUFFICIENT_TOOLS_MSG_ALT = (
"Only {tool_calls_count} tool calls so far; at least {min_tool_calls} are required. "
"Please call a tool to retrieve simulation data.{unused_hint}"
)
REACT_TOOL_LIMIT_MSG = (
"Tool-call budget exhausted ({tool_calls_count}/{max_tool_calls}); no more tool calls allowed. "
'Now, based on the information you have already gathered, output the section content prefixed with "Final Answer:".'
)
REACT_UNUSED_TOOLS_HINT = "\n💡 You haven't used: {unused_list} yet — try a different tool to get a multi-angle view."
REACT_FORCE_FINAL_MSG = "Tool-call limit reached. Please output Final Answer: directly and produce the section content."
# ── Chat prompt ──
CHAT_SYSTEM_PROMPT_TEMPLATE = """\
You are a concise and efficient simulation-prediction assistant.
[Background]
Prediction conditions: {simulation_requirement}
[Generated analytical report]
{report_content}
[Rules]
1. Prefer answering from the report above.
2. Answer the question directly; avoid lengthy meta-reasoning.
3. Only call tools when the report does not contain enough information to answer.
4. Keep your answers concise, clear, and well-structured.
[Available tools] (use only when needed; at most 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 long-form prose.
- Use the > format to quote the key source material.
- Lead with the conclusion, then explain the rationale."""
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(t('report.agentInitDone', graphId=graph_id, simulationId=simulation_id))
def _define_tools(self) -> Dict[str, Dict[str, Any]]:
"""定义可用工具"""
return {
"insight_forge": {
"name": "insight_forge",
"description": TOOL_DESC_INSIGHT_FORGE,
"parameters": {
"query": "The question or topic you want to analyze in depth.",
"report_context": "Current report-section context (optional; helps generate sharper sub-questions)."
}
},
"panorama_search": {
"name": "panorama_search",
"description": TOOL_DESC_PANORAMA_SEARCH,
"parameters": {
"query": "Search query, used 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 (e.g. 'Understand student opinion on the dorm 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(t('report.executingTool', toolName=tool_name, params=parameters))
try:
if tool_name == "insight_forge":
query = parameters.get("query", "")
ctx = parameters.get("report_context", "") or report_context
result = self.zep_tools.insight_forge(
graph_id=self.graph_id,
query=query,
simulation_requirement=self.simulation_requirement,
report_context=ctx
)
return result.to_text()
elif tool_name == "panorama_search":
# 广度搜索 - 获取全貌
query = parameters.get("query", "")
include_expired = parameters.get("include_expired", True)
if isinstance(include_expired, str):
include_expired = include_expired.lower() in ['true', '1', 'yes']
result = self.zep_tools.panorama_search(
graph_id=self.graph_id,
query=query,
include_expired=include_expired
)
return result.to_text()
elif tool_name == "quick_search":
# 简单搜索 - 快速检索
query = parameters.get("query", "")
limit = parameters.get("limit", 10)
if isinstance(limit, str):
limit = int(limit)
result = self.zep_tools.quick_search(
graph_id=self.graph_id,
query=query,
limit=limit
)
return result.to_text()
elif tool_name == "interview_agents":
# 深度采访 - 调用真实的OASIS采访API获取模拟Agent的回答（双平台）
interview_topic = parameters.get("interview_topic", parameters.get("query", ""))
max_agents = parameters.get("max_agents", 5)
if isinstance(max_agents, str):
max_agents = int(max_agents)
max_agents = min(max_agents, 10)
result = self.zep_tools.interview_agents(
simulation_id=self.simulation_id,
interview_requirement=interview_topic,
simulation_requirement=self.simulation_requirement,
max_agents=max_agents
)
return result.to_text()
# ========== 向后兼容的旧工具（内部重定向到新工具） ==========
elif tool_name == "search_graph":
# 重定向到 quick_search
logger.info(t('report.redirectToQuickSearch'))
return self._execute_tool("quick_search", parameters, report_context)
elif tool_name == "get_graph_statistics":
result = self.zep_tools.get_graph_statistics(self.graph_id)
return json.dumps(result, ensure_ascii=False, indent=2)
elif tool_name == "get_entity_summary":
entity_name = parameters.get("entity_name", "")
result = self.zep_tools.get_entity_summary(
graph_id=self.graph_id,
entity_name=entity_name
)
return json.dumps(result, ensure_ascii=False, indent=2)
elif tool_name == "get_simulation_context":
# 重定向到 insight_forge，因为它更强大
logger.info(t('report.redirectToInsightForge'))
query = parameters.get("query", self.simulation_requirement)
return self._execute_tool("insight_forge", {"query": query}, report_context)
elif tool_name == "get_entities_by_type":
entity_type = parameters.get("entity_type", "")
nodes = self.zep_tools.get_entities_by_type(
graph_id=self.graph_id,
entity_type=entity_type
)
result = [n.to_dict() for n in nodes]
return json.dumps(result, ensure_ascii=False, indent=2)
else:
return f"Unknown tool: {tool_name}. Please use one of: insight_forge, panorama_search, quick_search"
except Exception as e:
logger.error(t('report.toolExecFailed', toolName=tool_name, error=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(t('report.startPlanningOutline'))
if progress_callback:
progress_callback("planning", 0, t('progress.analyzingRequirements'))
# 首先获取模拟上下文
context = self.zep_tools.get_simulation_context(
graph_id=self.graph_id,
simulation_requirement=self.simulation_requirement
)
if progress_callback:
progress_callback("planning", 30, t('progress.generatingOutline'))
system_prompt = f"{PLAN_SYSTEM_PROMPT}\n\n{get_language_instruction()}"
user_prompt = PLAN_USER_PROMPT_TEMPLATE.format(
simulation_requirement=self.simulation_requirement,
total_nodes=context.get('graph_statistics', {}).get('total_nodes', 0),
total_edges=context.get('graph_statistics', {}).get('total_edges', 0),
entity_types=list(context.get('graph_statistics', {}).get('entity_types', {}).keys()),
total_entities=context.get('total_entities', 0),
related_facts_json=json.dumps(context.get('related_facts', [])[:10], ensure_ascii=False, indent=2),
)
try:
response = self.llm.chat_json(
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": user_prompt}
],
temperature=0.3
)
if progress_callback:
progress_callback("planning", 80, t('progress.parsingOutline'))
# 解析大纲
sections = []
for section_data in response.get("sections", []):
sections.append(ReportSection(
title=section_data.get("title", ""),
content=""
))
outline = ReportOutline(
title=response.get("title", "Simulation Analysis Report"),
summary=response.get("summary", ""),
sections=sections
)
if progress_callback:
progress_callback("planning", 100, t('progress.outlinePlanComplete'))
logger.info(t('report.outlinePlanDone', count=len(sections)))
return outline
except Exception as e:
logger.error(t('report.outlinePlanFailed', error=str(e)))
# 返回默认大纲（3个章节，作为fallback）
return ReportOutline(
title="Future Prediction Report",
summary="Trend and risk analysis grounded in simulation predictions.",
sections=[
ReportSection(title="Scenario and Key Findings"),
ReportSection(title="Population Behavior Predictions"),
ReportSection(title="Trend Outlook and Risk Notes")
]
)
def _generate_section_react(
self,
section: ReportSection,
outline: ReportOutline,
previous_sections: List[str],
progress_callback: Optional[Callable] = None,
section_index: int = 0
) -> str:
"""
使用ReACT模式生成单个章节内容
ReACT循环：
1. Thought（思考）- 分析需要什么信息
2. Action（行动）- 调用工具获取信息
3. Observation（观察）- 分析工具返回结果
4. 重复直到信息足够或达到最大次数
5. Final Answer（最终回答）- 生成章节内容
Args:
section: 要生成的章节
outline: 完整大纲
previous_sections: 之前章节的内容（用于保持连贯性）
progress_callback: 进度回调
section_index: 章节索引（用于日志记录）
Returns:
章节内容（Markdown格式）
"""
logger.info(t('report.reactGenerateSection', title=section.title))
# 记录章节开始日志
if self.report_logger:
self.report_logger.log_section_start(section.title, section_index)
system_prompt = SECTION_SYSTEM_PROMPT_TEMPLATE.format(
report_title=outline.title,
report_summary=outline.summary,
simulation_requirement=self.simulation_requirement,
section_title=section.title,
tools_description=self._get_tools_description(),
)
system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
# 构建用户prompt - 每个已完成章节各传入最大4000字
if previous_sections:
previous_parts = []
for sec in previous_sections:
# 每个章节最多4000字
truncated = sec[:4000] + "..." if len(sec) > 4000 else sec
previous_parts.append(truncated)
previous_content = "\n\n---\n\n".join(previous_parts)
else:
previous_content = "(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),
t('progress.deepSearchAndWrite', current=tool_calls_count, max=self.MAX_TOOL_CALLS_PER_SECTION)
)
# 调用LLM
response = self.llm.chat(
messages=messages,
temperature=0.5,
max_tokens=4096
)
# 检查 LLM 返回是否为 None（API 异常或内容为空）
if response is None:
logger.warning(t('report.sectionIterNone', title=section.title, iteration=iteration + 1))
# 如果还有迭代次数，添加消息并重试
if iteration < max_iterations - 1:
messages.append({"role": "assistant", "content": "(empty response)"})
messages.append({"role": "user", "content": "Please continue 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(t("log.report_agent.m001", response=response[:200]))
# 解析一次，复用结果
tool_calls = self._parse_tool_calls(response)
has_tool_calls = bool(tool_calls)
has_final_answer = "Final Answer:" in response
# ── 冲突处理：LLM 同时输出了工具调用和 Final Answer ──
if has_tool_calls and has_final_answer:
conflict_retries += 1
logger.warning(
t('report.sectionConflict', title=section.title, iteration=iteration+1, conflictCount=conflict_retries)
)
if conflict_retries <= 2:
# 前两次：丢弃本次响应，要求 LLM 重新回复
messages.append({"role": "assistant", "content": response})
messages.append({
"role": "user",
"content": (
"[Format error] You included both a tool call and a Final Answer in the same reply, which is not allowed.\n"
"Each reply may do exactly one of the following:\n"
"- Call a single tool (output one <tool_call> block; do NOT write Final Answer).\n"
"- Output the final content (prefix it with 'Final Answer:'; do NOT include <tool_call>).\n"
"Please reply again and do only one of the two."
),
})
continue
else:
# 第三次：降级处理，截断到第一个工具调用，强制执行
logger.warning(
t('report.sectionConflictDowngrade', title=section.title, conflictCount=conflict_retries)
)
first_tool_end = response.find('</tool_call>')
if first_tool_end != -1:
response = response[:first_tool_end + len('</tool_call>')]
tool_calls = self._parse_tool_calls(response)
has_tool_calls = bool(tool_calls)
has_final_answer = False
conflict_retries = 0
# 记录 LLM 响应日志
if self.report_logger:
self.report_logger.log_llm_response(
section_title=section.title,
section_index=section_index,
response=response,
iteration=iteration + 1,
has_tool_calls=has_tool_calls,
has_final_answer=has_final_answer
)
# ── 情况1：LLM 输出了 Final Answer ──
if has_final_answer:
# 工具调用次数不足，拒绝并要求继续调工具
if tool_calls_count < min_tool_calls:
messages.append({"role": "assistant", "content": response})
unused_tools = all_tools - used_tools
unused_hint = f"(These tools have not 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(t('report.sectionGenDone', title=section.title, count=tool_calls_count))
if self.report_logger:
self.report_logger.log_section_content(
section_title=section.title,
section_index=section_index,
content=final_answer,
tool_calls_count=tool_calls_count
)
return final_answer
# ── 情况2：LLM 尝试调用工具 ──
if has_tool_calls:
# 工具额度已耗尽 → 明确告知，要求输出 Final Answer
if tool_calls_count >= self.MAX_TOOL_CALLS_PER_SECTION:
messages.append({"role": "assistant", "content": response})
messages.append({
"role": "user",
"content": REACT_TOOL_LIMIT_MSG.format(
tool_calls_count=tool_calls_count,
max_tool_calls=self.MAX_TOOL_CALLS_PER_SECTION,
),
})
continue
# 只执行第一个工具调用
call = tool_calls[0]
if len(tool_calls) > 1:
logger.info(t('report.multiToolOnlyFirst', total=len(tool_calls), toolName=call['name']))
if self.report_logger:
self.report_logger.log_tool_call(
section_title=section.title,
section_index=section_index,
tool_name=call["name"],
parameters=call.get("parameters", {}),
iteration=iteration + 1
)
result = self._execute_tool(
call["name"],
call.get("parameters", {}),
report_context=report_context
)
if self.report_logger:
self.report_logger.log_tool_result(
section_title=section.title,
section_index=section_index,
tool_name=call["name"],
result=result,
iteration=iteration + 1
)
tool_calls_count += 1
used_tools.add(call['name'])
# 构建未使用工具提示
unused_tools = all_tools - used_tools
unused_hint = ""
if unused_tools and tool_calls_count < self.MAX_TOOL_CALLS_PER_SECTION:
unused_hint = REACT_UNUSED_TOOLS_HINT.format(unused_list=", ".join(unused_tools))
messages.append({"role": "assistant", "content": response})
messages.append({
"role": "user",
"content": REACT_OBSERVATION_TEMPLATE.format(
tool_name=call["name"],
result=result,
tool_calls_count=tool_calls_count,
max_tool_calls=self.MAX_TOOL_CALLS_PER_SECTION,
used_tools_str=", ".join(used_tools),
unused_hint=unused_hint,
),
})
continue
# ── 情况3：既没有工具调用，也没有 Final Answer ──
messages.append({"role": "assistant", "content": response})
if tool_calls_count < min_tool_calls:
# 工具调用次数不足，推荐未用过的工具
unused_tools = all_tools - used_tools
unused_hint = f"(These tools have not 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(t('report.sectionNoPrefix', title=section.title, count=tool_calls_count))
final_answer = response.strip()
if self.report_logger:
self.report_logger.log_section_content(
section_title=section.title,
section_index=section_index,
content=final_answer,
tool_calls_count=tool_calls_count
)
return final_answer
# 达到最大迭代次数，强制生成内容
logger.warning(t('report.sectionMaxIter', title=section.title))
messages.append({"role": "user", "content": REACT_FORCE_FINAL_MSG})
response = self.llm.chat(
messages=messages,
temperature=0.5,
max_tokens=4096
)
# 检查强制收尾时 LLM 返回是否为 None
if response is None:
logger.error(t('report.sectionForceFailed', title=section.title))
final_answer = t('report.sectionGenFailedContent')
elif "Final Answer:" in response:
final_answer = response.split("Final Answer:")[-1].strip()
else:
final_answer = response
# 记录章节内容生成完成日志
if self.report_logger:
self.report_logger.log_section_content(
section_title=section.title,
section_index=section_index,
content=final_answer,
tool_calls_count=tool_calls_count
)
return final_answer
def generate_report(
self,
progress_callback: Optional[Callable[[str, int, str], None]] = None,
report_id: Optional[str] = None
) -> Report:
"""
生成完整报告（分章节实时输出）
每个章节生成完成后立即保存到文件夹，不需要等待整个报告完成。
文件结构：
reports/{report_id}/
meta.json - 报告元信息
outline.json - 报告大纲
progress.json - 生成进度
section_01.md - 第1章节
section_02.md - 第2章节
...
full_report.md - 完整报告
Args:
progress_callback: 进度回调函数 (stage, progress, message)
report_id: 报告ID（可选，如果不传则自动生成）
Returns:
Report: 完整报告
"""
import uuid
# 如果没有传入 report_id，则自动生成
if not report_id:
report_id = f"report_{uuid.uuid4().hex[:12]}"
start_time = datetime.now()
report = Report(
report_id=report_id,
simulation_id=self.simulation_id,
graph_id=self.graph_id,
simulation_requirement=self.simulation_requirement,
status=ReportStatus.PENDING,
created_at=datetime.now().isoformat()
)
# 已完成的章节标题列表（用于进度追踪）
completed_section_titles = []
try:
# 初始化：创建报告文件夹并保存初始状态
ReportManager._ensure_report_folder(report_id)
# 初始化日志记录器（结构化日志 agent_log.jsonl）
self.report_logger = ReportLogger(report_id)
self.report_logger.log_start(
simulation_id=self.simulation_id,
graph_id=self.graph_id,
simulation_requirement=self.simulation_requirement
)
# 初始化控制台日志记录器（console_log.txt）
self.console_logger = ReportConsoleLogger(report_id)
ReportManager.update_progress(
report_id, "pending", 0, t('progress.initReport'),
completed_sections=[]
)
ReportManager.save_report(report)
# 阶段1: 规划大纲
report.status = ReportStatus.PLANNING
ReportManager.update_progress(
report_id, "planning", 5, t('progress.startPlanningOutline'),
completed_sections=[]
)
# 记录规划开始日志
self.report_logger.log_planning_start()
if progress_callback:
progress_callback("planning", 0, t('progress.startPlanningOutline'))
outline = self.plan_outline(
progress_callback=lambda stage, prog, msg:
progress_callback(stage, prog // 5, msg) if progress_callback else None
)
report.outline = outline
# 记录规划完成日志
self.report_logger.log_planning_complete(outline.to_dict())
# 保存大纲到文件
ReportManager.save_outline(report_id, outline)
ReportManager.update_progress(
report_id, "planning", 15, t('progress.outlineDone', count=len(outline.sections)),
completed_sections=[]
)
ReportManager.save_report(report)
logger.info(t('report.outlineSavedToFile', reportId=report_id))
# 阶段2: 逐章节生成（分章节保存）
report.status = ReportStatus.GENERATING
total_sections = len(outline.sections)
generated_sections = [] # 保存内容用于上下文
for i, section in enumerate(outline.sections):
section_num = i + 1
base_progress = 20 + int((i / total_sections) * 70)
# 更新进度
ReportManager.update_progress(
report_id, "generating", base_progress,
t('progress.generatingSection', title=section.title, current=section_num, total=total_sections),
current_section=section.title,
completed_sections=completed_section_titles
)
if progress_callback:
progress_callback(
"generating",
base_progress,
t('progress.generatingSection', title=section.title, current=section_num, total=total_sections)
)
# 生成主章节内容
section_content = self._generate_section_react(
section=section,
outline=outline,
previous_sections=generated_sections,
progress_callback=lambda stage, prog, msg:
progress_callback(
stage,
base_progress + int(prog * 0.7 / total_sections),
msg
) if progress_callback else None,
section_index=section_num
)
section.content = section_content
generated_sections.append(f"## {section.title}\n\n{section_content}")
# 保存章节
ReportManager.save_section(report_id, section_num, section)
completed_section_titles.append(section.title)
# 记录章节完成日志
full_section_content = f"## {section.title}\n\n{section_content}"
if self.report_logger:
self.report_logger.log_section_full_complete(
section_title=section.title,
section_index=section_num,
full_content=full_section_content.strip()
)
logger.info(t('report.sectionSaved', reportId=report_id, sectionNum=f"{section_num:02d}"))
# 更新进度
ReportManager.update_progress(
report_id, "generating",
base_progress + int(70 / total_sections),
t('progress.sectionDone', title=section.title),
current_section=None,
completed_sections=completed_section_titles
)
# 阶段3: 组装完整报告
if progress_callback:
progress_callback("generating", 95, t('progress.assemblingReport'))
ReportManager.update_progress(
report_id, "generating", 95, t('progress.assemblingReport'),
completed_sections=completed_section_titles
)
# 使用ReportManager组装完整报告
report.markdown_content = ReportManager.assemble_full_report(report_id, outline)
report.status = ReportStatus.COMPLETED
report.completed_at = datetime.now().isoformat()
# 计算总耗时
total_time_seconds = (datetime.now() - start_time).total_seconds()
# 记录报告完成日志
if self.report_logger:
self.report_logger.log_report_complete(
total_sections=total_sections,
total_time_seconds=total_time_seconds
)
# 保存最终报告
ReportManager.save_report(report)
ReportManager.update_progress(
report_id, "completed", 100, t('progress.reportComplete'),
completed_sections=completed_section_titles
)
if progress_callback:
progress_callback("completed", 100, t('progress.reportComplete'))
logger.info(t('report.reportGenDone', reportId=report_id))
# 关闭控制台日志记录器
if self.console_logger:
self.console_logger.close()
self.console_logger = None
return report
except Exception as e:
logger.error(t('report.reportGenFailed', error=str(e)))
report.status = ReportStatus.FAILED
report.error = str(e)
# 记录错误日志
if self.report_logger:
self.report_logger.log_error(str(e), "failed")
# 保存失败状态
try:
ReportManager.save_report(report)
ReportManager.update_progress(
report_id, "failed", -1, t('progress.reportFailed', error=str(e)),
completed_sections=completed_section_titles
)
except Exception:
pass # 忽略保存失败的错误
# 关闭控制台日志记录器
if self.console_logger:
self.console_logger.close()
self.console_logger = None
return report
def chat(
self,
message: str,
chat_history: List[Dict[str, str]] = None
) -> Dict[str, Any]:
"""
与Report Agent对话
在对话中Agent可以自主调用检索工具来回答问题
Args:
message: 用户消息
chat_history: 对话历史
Returns:
{
"response": "Agent回复",
"tool_calls": [调用的工具列表],
"sources": [信息来源]
}
"""
logger.info(t('report.agentChat', message=message[:50]))
chat_history = chat_history or []
# 获取已生成的报告内容
report_content = ""
try:
report = ReportManager.get_report_by_simulation(self.simulation_id)
if report and report.markdown_content:
# 限制报告长度，避免上下文过长
report_content = report.markdown_content[:15000]
if len(report.markdown_content) > 15000:
report_content += "\n\n... [report content truncated] ..."
except Exception as e:
logger.warning(t('report.fetchReportFailed', error=e))
system_prompt = CHAT_SYSTEM_PROMPT_TEMPLATE.format(
simulation_requirement=self.simulation_requirement,
report_content=report_content if report_content else "(no report yet)",
tools_description=self._get_tools_description(),
)
system_prompt = f"{system_prompt}\n\n{get_language_instruction()}"
# 构建消息
messages = [{"role": "system", "content": system_prompt}]
# 添加历史对话
for h in chat_history[-10:]: # 限制历史长度
messages.append(h)
# 添加用户消息
messages.append({
"role": "user",
"content": message
})
# ReACT循环（简化版）
tool_calls_made = []
max_iterations = 2 # 减少迭代轮数
for iteration in range(max_iterations):
response = self.llm.chat(
messages=messages,
temperature=0.5
)
# 解析工具调用
tool_calls = self._parse_tool_calls(response)
if not tool_calls:
# 没有工具调用，直接返回响应
clean_response = re.sub(r'<tool_call>.*?</tool_call>', '', response, flags=re.DOTALL)
clean_response = re.sub(r'\[TOOL_CALL\].*?\)', '', clean_response)
return {
"response": clean_response.strip(),
"tool_calls": tool_calls_made,
"sources": [tc.get("parameters", {}).get("query", "") for tc in tool_calls_made]
}
# 执行工具调用（限制数量）
tool_results = []
for call in tool_calls[:1]: # 每轮最多执行1次工具调用
if len(tool_calls_made) >= self.MAX_TOOL_CALLS_PER_CHAT:
break
result = self._execute_tool(call["name"], call.get("parameters", {}))
tool_results.append({
"tool": call["name"],
"result": result[:1500] # 限制结果长度
})
tool_calls_made.append(call)
# 将结果添加到消息
messages.append({"role": "assistant", "content": response})
observation = "\n".join([f"[{r['tool']} 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(t('report.outlineSaved', reportId=report_id))
@classmethod
def save_section(
cls,
report_id: str,
section_index: int,
section: ReportSection
) -> str:
"""
保存单个章节
在每个章节生成完成后立即调用，实现分章节输出
Args:
report_id: 报告ID
section_index: 章节索引（从1开始）
section: 章节对象
Returns:
保存的文件路径
"""
cls._ensure_report_folder(report_id)
# 构建章节Markdown内容 - 清理可能存在的重复标题
cleaned_content = cls._clean_section_content(section.content, section.title)
md_content = f"## {section.title}\n\n"
if cleaned_content:
md_content += f"{cleaned_content}\n\n"
# 保存文件
file_suffix = f"section_{section_index:02d}.md"
file_path = os.path.join(cls._get_report_folder(report_id), file_suffix)
with open(file_path, 'w', encoding='utf-8') as f:
f.write(md_content)
logger.info(t('report.sectionFileSaved', reportId=report_id, fileSuffix=file_suffix))
return file_path
@classmethod
def _clean_section_content(cls, content: str, section_title: str) -> str:
"""
清理章节内容
1. 移除内容开头与章节标题重复的Markdown标题行
2. 将所有 ### 及以下级别的标题转换为粗体文本
Args:
content: 原始内容
section_title: 章节标题
Returns:
清理后的内容
"""
import re
if not content:
return content
content = content.strip()
lines = content.split('\n')
cleaned_lines = []
skip_next_empty = False
for i, line in enumerate(lines):
stripped = line.strip()
# 检查是否是Markdown标题行
heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
if heading_match:
level = len(heading_match.group(1))
title_text = heading_match.group(2).strip()
# 检查是否是与章节标题重复的标题（跳过前5行内的重复）
if i < 5:
if title_text == section_title or title_text.replace(' ', '') == section_title.replace(' ', ''):
skip_next_empty = True
continue
# 将所有级别的标题（#, ##, ###, ####等）转换为粗体
# 因为章节标题由系统添加，内容中不应有任何标题
cleaned_lines.append(f"**{title_text}**")
cleaned_lines.append("") # 添加空行
continue
# 如果上一行是被跳过的标题，且当前行为空，也跳过
if skip_next_empty and stripped == '':
skip_next_empty = False
continue
skip_next_empty = False
cleaned_lines.append(line)
# 移除开头的空行
while cleaned_lines and cleaned_lines[0].strip() == '':
cleaned_lines.pop(0)
# 移除开头的分隔线
while cleaned_lines and cleaned_lines[0].strip() in ['---', '***', '___']:
cleaned_lines.pop(0)
# 同时移除分隔线后的空行
while cleaned_lines and cleaned_lines[0].strip() == '':
cleaned_lines.pop(0)
return '\n'.join(cleaned_lines)
@classmethod
def update_progress(
cls,
report_id: str,
status: str,
progress: int,
message: str,
current_section: str = None,
completed_sections: List[str] = None
) -> None:
"""
更新报告生成进度
前端可以通过读取progress.json获取实时进度
"""
cls._ensure_report_folder(report_id)
progress_data = {
"status": status,
"progress": progress,
"message": message,
"current_section": current_section,
"completed_sections": completed_sections or [],
"updated_at": datetime.now().isoformat()
}
with open(cls._get_progress_path(report_id), 'w', encoding='utf-8') as f:
json.dump(progress_data, f, ensure_ascii=False, indent=2)
@classmethod
def get_progress(cls, report_id: str) -> Optional[Dict[str, Any]]:
"""获取报告生成进度"""
path = cls._get_progress_path(report_id)
if not os.path.exists(path):
return None
with open(path, 'r', encoding='utf-8') as f:
return json.load(f)
@classmethod
def get_generated_sections(cls, report_id: str) -> List[Dict[str, Any]]:
"""
获取已生成的章节列表
返回所有已保存的章节文件信息
"""
folder = cls._get_report_folder(report_id)
if not os.path.exists(folder):
return []
sections = []
for filename in sorted(os.listdir(folder)):
if filename.startswith('section_') and filename.endswith('.md'):
file_path = os.path.join(folder, filename)
with open(file_path, 'r', encoding='utf-8') as f:
content = f.read()
# 从文件名解析章节索引
parts = filename.replace('.md', '').split('_')
section_index = int(parts[1])
sections.append({
"filename": filename,
"section_index": section_index,
"content": content
})
return sections
@classmethod
def assemble_full_report(cls, report_id: str, outline: ReportOutline) -> str:
"""
组装完整报告
从已保存的章节文件组装完整报告，并进行标题清理
"""
folder = cls._get_report_folder(report_id)
# 构建报告头部
md_content = f"# {outline.title}\n\n"
md_content += f"> {outline.summary}\n\n"
md_content += f"---\n\n"
# 按顺序读取所有章节文件
sections = cls.get_generated_sections(report_id)
for section_info in sections:
md_content += section_info["content"]
# 后处理：清理整个报告的标题问题
md_content = cls._post_process_report(md_content, outline)
# 保存完整报告
full_path = cls._get_report_markdown_path(report_id)
with open(full_path, 'w', encoding='utf-8') as f:
f.write(md_content)
logger.info(t('report.fullReportAssembled', reportId=report_id))
return md_content
@classmethod
def _post_process_report(cls, content: str, outline: ReportOutline) -> str:
"""
后处理报告内容
1. 移除重复的标题
2. 保留报告主标题(#)和章节标题(##)，移除其他级别的标题(###, ####等)
3. 清理多余的空行和分隔线
Args:
content: 原始报告内容
outline: 报告大纲
Returns:
处理后的内容
"""
import re
lines = content.split('\n')
processed_lines = []
prev_was_heading = False
# 收集大纲中的所有章节标题
section_titles = set()
for section in outline.sections:
section_titles.add(section.title)
i = 0
while i < len(lines):
line = lines[i]
stripped = line.strip()
# 检查是否是标题行
heading_match = re.match(r'^(#{1,6})\s+(.+)$', stripped)
if heading_match:
level = len(heading_match.group(1))
title = heading_match.group(2).strip()
# 检查是否是重复标题（在连续5行内出现相同内容的标题）
is_duplicate = False
for j in range(max(0, len(processed_lines) - 5), len(processed_lines)):
prev_line = processed_lines[j].strip()
prev_match = re.match(r'^(#{1,6})\s+(.+)$', prev_line)
if prev_match:
prev_title = prev_match.group(2).strip()
if prev_title == title:
is_duplicate = True
break
if is_duplicate:
# 跳过重复标题及其后的空行
i += 1
while i < len(lines) and lines[i].strip() == '':
i += 1
continue
# 标题层级处理：
# - # (level=1) 只保留报告主标题
# - ## (level=2) 保留章节标题
# - ### 及以下 (level>=3) 转换为粗体文本
if level == 1:
if title == outline.title:
# 保留报告主标题
processed_lines.append(line)
prev_was_heading = True
elif title in section_titles:
# 章节标题错误使用了#，修正为##
processed_lines.append(f"## {title}")
prev_was_heading = True
else:
# 其他一级标题转为粗体
processed_lines.append(f"**{title}**")
processed_lines.append("")
prev_was_heading = False
elif level == 2:
if title in section_titles or title == outline.title:
# 保留章节标题
processed_lines.append(line)
prev_was_heading = True
else:
# 非章节的二级标题转为粗体
processed_lines.append(f"**{title}**")
processed_lines.append("")
prev_was_heading = False
else:
# ### 及以下级别的标题转换为粗体文本
processed_lines.append(f"**{title}**")
processed_lines.append("")
prev_was_heading = False
i += 1
continue
elif stripped == '---' and prev_was_heading:
# 跳过标题后紧跟的分隔线
i += 1
continue
elif stripped == '' and prev_was_heading:
# 标题后只保留一个空行
if processed_lines and processed_lines[-1].strip() != '':
processed_lines.append(line)
prev_was_heading = False
else:
processed_lines.append(line)
prev_was_heading = False
i += 1
# 清理连续的多个空行（保留最多2个）
result_lines = []
empty_count = 0
for line in processed_lines:
if line.strip() == '':
empty_count += 1
if empty_count <= 2:
result_lines.append(line)
else:
empty_count = 0
result_lines.append(line)
return '\n'.join(result_lines)
@classmethod
def save_report(cls, report: Report) -> None:
"""保存报告元信息和完整报告"""
cls._ensure_report_folder(report.report_id)
# 保存元信息JSON
with open(cls._get_report_path(report.report_id), 'w', encoding='utf-8') as f:
json.dump(report.to_dict(), f, ensure_ascii=False, indent=2)
# 保存大纲
if report.outline:
cls.save_outline(report.report_id, report.outline)
# 保存完整Markdown报告
if report.markdown_content:
with open(cls._get_report_markdown_path(report.report_id), 'w', encoding='utf-8') as f:
f.write(report.markdown_content)
logger.info(t('report.reportSaved', reportId=report.report_id))
@classmethod
def get_report(cls, report_id: str) -> Optional[Report]:
"""获取报告"""
path = cls._get_report_path(report_id)
if not os.path.exists(path):
# 兼容旧格式：检查直接存储在reports目录下的文件
old_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
if os.path.exists(old_path):
path = old_path
else:
return None
with open(path, 'r', encoding='utf-8') as f:
data = json.load(f)
# 重建Report对象
outline = None
if data.get('outline'):
outline_data = data['outline']
sections = []
for s in outline_data.get('sections', []):
sections.append(ReportSection(
title=s['title'],
content=s.get('content', '')
))
outline = ReportOutline(
title=outline_data['title'],
summary=outline_data['summary'],
sections=sections
)
# 如果markdown_content为空，尝试从full_report.md读取
markdown_content = data.get('markdown_content', '')
if not markdown_content:
full_report_path = cls._get_report_markdown_path(report_id)
if os.path.exists(full_report_path):
with open(full_report_path, 'r', encoding='utf-8') as f:
markdown_content = f.read()
return Report(
report_id=data['report_id'],
simulation_id=data['simulation_id'],
graph_id=data['graph_id'],
simulation_requirement=data['simulation_requirement'],
status=ReportStatus(data['status']),
outline=outline,
markdown_content=markdown_content,
created_at=data.get('created_at', ''),
completed_at=data.get('completed_at', ''),
error=data.get('error')
)
@classmethod
def get_report_by_simulation(cls, simulation_id: str) -> Optional[Report]:
"""根据模拟ID获取报告"""
cls._ensure_reports_dir()
for item in os.listdir(cls.REPORTS_DIR):
item_path = os.path.join(cls.REPORTS_DIR, item)
# 新格式：文件夹
if os.path.isdir(item_path):
report = cls.get_report(item)
if report and report.simulation_id == simulation_id:
return report
# 兼容旧格式：JSON文件
elif item.endswith('.json'):
report_id = item[:-5]
report = cls.get_report(report_id)
if report and report.simulation_id == simulation_id:
return report
return None
@classmethod
def list_reports(cls, simulation_id: Optional[str] = None, limit: int = 50) -> List[Report]:
"""列出报告"""
cls._ensure_reports_dir()
reports = []
for item in os.listdir(cls.REPORTS_DIR):
item_path = os.path.join(cls.REPORTS_DIR, item)
# 新格式：文件夹
if os.path.isdir(item_path):
report = cls.get_report(item)
if report:
if simulation_id is None or report.simulation_id == simulation_id:
reports.append(report)
# 兼容旧格式：JSON文件
elif item.endswith('.json'):
report_id = item[:-5]
report = cls.get_report(report_id)
if report:
if simulation_id is None or report.simulation_id == simulation_id:
reports.append(report)
# 按创建时间倒序
reports.sort(key=lambda r: r.created_at, reverse=True)
return reports[:limit]
@classmethod
def delete_report(cls, report_id: str) -> bool:
"""删除报告（整个文件夹）"""
import shutil
folder_path = cls._get_report_folder(report_id)
# 新格式：删除整个文件夹
if os.path.exists(folder_path) and os.path.isdir(folder_path):
shutil.rmtree(folder_path)
logger.info(t('report.reportFolderDeleted', reportId=report_id))
return True
# 兼容旧格式：删除单独的文件
deleted = False
old_json_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.json")
old_md_path = os.path.join(cls.REPORTS_DIR, f"{report_id}.md")
if os.path.exists(old_json_path):
os.remove(old_json_path)
deleted = True
if os.path.exists(old_md_path):
os.remove(old_md_path)
deleted = True
return deleted
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