269 lines
9.4 KiB
Python
269 lines
9.4 KiB
Python
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"""
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信息图 LLM 报告导出器
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调用大模型生成信息图展示的格式化内容
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"""
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import json
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import os
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import re
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from datetime import datetime
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from typing import Dict, Any, Optional
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class InfographicLLMExporter:
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"""信息图 LLM 报告导出器 - 调用大模型生成信息图内容"""
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LLM_CONFIG = {
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'OPENAI_API_BASE': None,
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'OPENAI_API_KEY': None,
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'OPENAI_API_MODEL': None,
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}
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PROMPT_TEMPLATE = """你是一位专业的可视化设计师和数据分析专家。你的任务是将以下任务执行数据生成适合信息图展示的格式化内容。
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## 任务基本信息
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- 任务名称：{task_name}
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## 任务大纲（规划阶段）
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{task_outline}
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## 执行结果
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{rehearsal_log}
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## 参与智能体
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{agents}
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## 智能体评分
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{agent_scores}
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---
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## 信息图内容要求
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请生成以下信息图展示内容（JSON 格式输出）：
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{{
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"summary": "执行摘要 - 2-3句话概括整体执行情况",
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"highlights": [
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"亮点1 - 取得的显著成果",
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"亮点2 - 关键突破或创新",
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"亮点3 - 重要的里程碑"
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],
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"statistics": {{
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"total_steps": 执行总步骤数,
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"agent_count": 参与智能体数量,
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"completion_rate": 完成率(百分比),
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"quality_score": 质量评分(1-10)
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}},
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"key_insights": [
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"关键洞察1 - 从执行过程中总结的洞见",
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"关键洞察2 - 值得关注的趋势或模式",
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"关键洞察3 - 对未来工作的建议"
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],
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"timeline": [
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{{"step": "步骤名称", "status": "完成/进行中/未完成", "key_result": "关键产出"}},
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...
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],
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"agent_performance": [
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{{"name": "智能体名称", "score": 评分, "contribution": "主要贡献"}},
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...
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]
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}}
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---
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## 输出要求
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- 输出必须是有效的 JSON 格式
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- 语言：简体中文
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- 所有字符串值使用中文
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- statistics 中的数值必须是整数或浮点数
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- 确保 JSON 格式正确，不要有语法错误
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- 不要输出 JSON 之外的任何内容
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- **重要**："执行结果"（rehearsal_log）只是参考数据，用于帮助你分析整体执行情况生成摘要、亮点、统计数据等。**不要在任何输出字段中直接复制或输出原始执行结果数据**，而应该对这些数据进行分析和提炼，生成适合信息图展示的格式化内容。
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"""
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def __init__(self):
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self._load_llm_config()
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def _load_llm_config(self):
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"""从配置文件加载 LLM 配置"""
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try:
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import yaml
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possible_paths = [
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os.path.join(os.path.dirname(os.path.dirname(__file__)), 'config', 'config.yaml'),
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os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'backend', 'config', 'config.yaml'),
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os.path.join(os.getcwd(), 'config', 'config.yaml'),
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]
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for config_path in possible_paths:
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if os.path.exists(config_path):
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with open(config_path, 'r', encoding='utf-8') as f:
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config = yaml.safe_load(f)
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if config:
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self.LLM_CONFIG['OPENAI_API_BASE'] = config.get('OPENAI_API_BASE')
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self.LLM_CONFIG['OPENAI_API_KEY'] = config.get('OPENAI_API_KEY')
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self.LLM_CONFIG['OPENAI_API_MODEL'] = config.get('OPENAI_API_MODEL')
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print(f"已加载 LLM 配置: {self.LLM_CONFIG['OPENAI_API_MODEL']}")
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return
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except Exception as e:
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print(f"加载 LLM 配置失败: {e}")
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def generate(self, task_data: Dict[str, Any]) -> Dict[str, Any]:
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"""生成信息图内容（调用 LLM 生成）"""
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try:
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task_name = task_data.get('task_name', '未命名任务')
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task_outline = task_data.get('task_outline')
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rehearsal_log = task_data.get('rehearsal_log')
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agent_scores = task_data.get('agent_scores')
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agents = self._extract_agents(task_outline)
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filtered_agent_scores = self._filter_agent_scores(agent_scores, agents)
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task_outline_str = json.dumps(task_outline, ensure_ascii=False, indent=2) if task_outline else '无'
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rehearsal_log_str = json.dumps(rehearsal_log, ensure_ascii=False, indent=2) if rehearsal_log else '无'
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agents_str = ', '.join(agents) if agents else '无'
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agent_scores_str = json.dumps(filtered_agent_scores, ensure_ascii=False, indent=2) if filtered_agent_scores else '无'
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prompt = self.PROMPT_TEMPLATE.format(
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task_name=task_name,
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task_outline=task_outline_str,
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rehearsal_log=rehearsal_log_str,
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agents=agents_str,
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agent_scores=agent_scores_str
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)
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print("正在调用大模型生成信息图内容...")
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llm_result = self._call_llm(prompt)
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if not llm_result:
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print("LLM 生成信息图内容失败")
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return None
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infographic_data = self._parse_llm_result(llm_result)
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if not infographic_data:
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print("解析 LLM 结果失败")
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return None
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print(f"信息图内容生成成功")
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return infographic_data
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except Exception as e:
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print(f"信息图 LLM 生成失败: {e}")
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import traceback
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traceback.print_exc()
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return None
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def _extract_agents(self, task_outline: Any) -> list:
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"""从 task_outline 中提取参与智能体列表"""
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agents = set()
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if not task_outline or not isinstance(task_outline, dict):
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return []
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collaboration_process = task_outline.get('Collaboration Process', [])
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if not collaboration_process or not isinstance(collaboration_process, list):
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return []
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for step in collaboration_process:
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if isinstance(step, dict):
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agent_selection = step.get('AgentSelection', [])
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if isinstance(agent_selection, list):
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for agent in agent_selection:
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if agent:
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agents.add(agent)
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return list(agents)
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def _filter_agent_scores(self, agent_scores: Any, agents: list) -> dict:
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"""过滤 agent_scores，只保留参与当前任务的智能体评分"""
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if not agent_scores or not isinstance(agent_scores, dict):
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return {}
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if not agents:
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return {}
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filtered = {}
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for step_id, step_data in agent_scores.items():
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if not isinstance(step_data, dict):
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continue
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aspect_list = step_data.get('aspectList', [])
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agent_scores_data = step_data.get('agentScores', {})
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if not agent_scores_data:
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continue
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filtered_scores = {}
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for agent_name, scores in agent_scores_data.items():
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if agent_name in agents and isinstance(scores, dict):
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filtered_scores[agent_name] = scores
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if filtered_scores:
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filtered[step_id] = {
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'aspectList': aspect_list,
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'agentScores': filtered_scores
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}
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return filtered
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def _call_llm(self, prompt: str) -> str:
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"""调用大模型 API 生成内容"""
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try:
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import openai
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if not self.LLM_CONFIG['OPENAI_API_KEY']:
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print("错误: OPENAI_API_KEY 未配置")
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return ""
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if not self.LLM_CONFIG['OPENAI_API_BASE']:
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print("错误: OPENAI_API_BASE 未配置")
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return ""
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if not self.LLM_CONFIG['OPENAI_API_MODEL']:
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print("错误: OPENAI_API_MODEL 未配置")
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return ""
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client = openai.OpenAI(
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api_key=self.LLM_CONFIG['OPENAI_API_KEY'],
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base_url=self.LLM_CONFIG['OPENAI_API_BASE']
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)
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response = client.chat.completions.create(
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model=self.LLM_CONFIG['OPENAI_API_MODEL'],
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messages=[
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{"role": "user", "content": prompt}
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],
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temperature=0.7,
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max_tokens=8000,
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)
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if response and response.choices:
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return response.choices[0].message.content
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return ""
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except ImportError:
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print("请安装 openai 库: pip install openai")
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return ""
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except Exception as e:
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print(f"调用 LLM 失败: {e}")
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return ""
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def _parse_llm_result(self, llm_result: str) -> Optional[Dict[str, Any]]:
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"""解析 LLM 返回的 JSON 字符串"""
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try:
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json_str = llm_result.strip()
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if json_str.startswith("```json"):
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json_str = json_str[7:]
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if json_str.startswith("```"):
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json_str = json_str[3:]
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if json_str.endswith("```"):
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json_str = json_str[:-3]
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json_str = json_str.strip()
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return json.loads(json_str)
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except json.JSONDecodeError as e:
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print(f"JSON 解析失败: {e}")
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print(f"原始结果: {llm_result[:500]}...")
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return None
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except Exception as e:
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print(f"解析失败: {e}")
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return None
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