545 lines
19 KiB
Python
545 lines
19 KiB
Python
import asyncio
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import re
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import random
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from typing import TypedDict, List, Dict, Any, Literal
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from langgraph.graph import StateGraph, START, END
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import os
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import sys
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current_dir = os.path.dirname(os.path.abspath(__file__))
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parent_dir = os.path.dirname(current_dir)
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if parent_dir not in sys.path:
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sys.path.append(parent_dir)
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from modelsAPI.model_api import OpenaiAPI
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from utils.function_tracker import track_function_calls
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from utils.word_generator import generate_report_word
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from config import SERVER_CONFIG
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import json
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from workflows.history_manager import init_history, build_history_str
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def _extract_images_from_text(text: str) -> List[Dict[str, str]]:
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"""从文本中提取图片URL和描述信息"""
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if not text:
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return []
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images = []
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url_pattern = r'https?://[^\s<>"{}|\\^`\[\]]+\.(?:png|jpg|jpeg|gif|bmp|webp)'
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urls = re.findall(url_pattern, text, re.IGNORECASE)
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for url in urls:
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summary = ""
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url_index = text.find(url)
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if url_index != -1:
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after_url = text[url_index + len(url):]
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result_match = re.search(
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r'(?:\*\*图片识别结果：\*\*|图片识别结果[：:]|识别结果[：:])\s*(.*?)(?=\n\n\n|\n\n[A-Za-z\u4e00-\u9fa5]|$)',
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after_url,
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re.DOTALL
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)
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if result_match:
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summary = result_match.group(1).strip()[:300]
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images.append({
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"url": url,
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"summary": summary or "用户上传的图片"
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})
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return images
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class HistoryQAAnalysisState(TypedDict):
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"""历史问答分析工作流状态"""
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extracted_text: str
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combined_query: str
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history_message: str
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history_messages: List[Dict[str, Any]]
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extracted_items: List[Dict[str, Any]]
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extracted_images: List[Dict[str, str]]
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report_title: str
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report_text: str
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response: str
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actions: List[Dict[str, Any]]
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route_flag: Literal["", "report"]
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suggestedReplies: List[Dict[str, Any]]
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error_message: str
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async def extract_analysis_items(state: HistoryQAAnalysisState) -> Dict[str, Any]:
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from utils.function_tracker import get_all_callbacks
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combined_query = state.get("combined_query", "") or state.get("extracted_text", "") or ""
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history_messages = state.get("history_messages", [])
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history_message = state.get("history_message", "")
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pre_extracted_images = _extract_images_from_text(history_message)
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history_context = build_history_str(history_messages, recent_count=10, assistant_truncate=200)
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if not history_context and history_message:
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history_context = history_message
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loading_prompts = [
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{"title": "提取相关历史问答数据中...", "details": f"历史消息数: {len(history_messages)}条"},
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{"title": "🔍 正在分析历史对话内容...", "details": f"共处理 {len(history_messages)} 条消息"},
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{"title": "📝 解析历史问答信息中...", "details": f"已加载 {len(history_messages)} 条历史记录"},
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{"title": "📊 整理历史对话数据...", "details": f"当前历史消息: {len(history_messages)} 条"},
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{"title": "🔑 提取对话关键信息...", "details": f"历史对话总数: {len(history_messages)}条"},
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]
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selected_prompt = random.choice(loading_prompts)
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parent_event = {
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"type": "function_execution",
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"title": selected_prompt["title"],
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"details": selected_prompt["details"],
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}
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for callback in get_all_callbacks():
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try:
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callback(parent_event)
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except Exception:
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pass
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prompt = f"""
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你是一名情报分析与知识管理专家。
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请从以下【用户输入】中抽取用于统计分析的结构化信息。
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【用户输入】
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{combined_query}
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【历史对话上下文】
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{history_context if history_context else "（无）"}
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【抽取规则】
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输出一个JSON对象，包含以下字段：
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- report_title：报告标题（根据历史对话内容生成一个简洁的标题，格式为"XXX故障处理报告"，不超过20个字）
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- items：抽取记录数组，每条记录包含：
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- topic：问题主题（如 设备、系统、管理、流程、保障、训练 等）
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- key_entity：关键对象或名词
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- conclusion：结论 / 判断 / 回答要点
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- action_suggestion：建议或行动（若无可为空）
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- date：时间（若无法判断可为空）
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- images：图片信息数组（仅当有用户上传图片时提取，每条记录包含）：
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- url：图片URL（直接从【用户上传图片信息】中提取）
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- summary：图片内容摘要（根据图片识别结果提炼关键信息，如设备类型、故障现象、异常情况等，不超过100字）
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【输出要求】
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1. 仅输出 JSON 对象
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2. 不要附加任何解释
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3. 如果没有图片信息，images字段返回空数组
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"""
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try:
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result = await OpenaiAPI.open_api_chat_without_thinking(
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query=prompt,
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model=None,
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json_output=True
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)
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parsed_result = json.loads(result)
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if isinstance(parsed_result, dict):
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extracted_items = parsed_result.get("items", [])
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report_title = parsed_result.get("report_title", "历史问答分析报告")
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model_images = parsed_result.get("images", [])
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else:
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extracted_items = parsed_result if isinstance(parsed_result, list) else []
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report_title = "历史问答分析报告"
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model_images = []
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except Exception as e:
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extracted_items = []
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report_title = "历史问答分析报告"
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model_images = []
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final_images = pre_extracted_images if pre_extracted_images else model_images
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return {"extracted_items": extracted_items, "report_title": report_title, "extracted_images": final_images}
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# @track_function_calls
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async def generate_analysis_report_text(state: HistoryQAAnalysisState) -> Dict[str, Any]:
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from utils.function_tracker import get_all_callbacks
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import time
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extracted_items = state.get("extracted_items", [])
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report_title = state.get("report_title", "历史问答分析报告")
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combined_query = state.get("combined_query", "") or state.get("extracted_text", "") or ""
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history_messages = state.get("history_messages", [])
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extracted_images = state.get("extracted_images", [])
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history_message = state.get("history_message", "")
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def summarize_extracted_items(items) -> str:
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"""将抽取结果转为中文描述，兼容 list / dict"""
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if not items:
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return "未提取到有效分析数据"
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# 如果是单个 dict，转为 list
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if isinstance(items, dict):
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items = [items]
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# 如果不是 list，直接返回
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if not isinstance(items, list):
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return "分析数据格式异常"
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topics = set()
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entities = set()
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for item in items:
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if not isinstance(item, dict):
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continue
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topic = item.get("topic")
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entity = item.get("key_entity")
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if topic:
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topics.add(str(topic))
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if entity:
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entities.add(str(entity))
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topic_text = "、".join(list(topics)[:5]) if topics else "未识别"
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entity_text = "、".join(list(entities)[:5]) if entities else "未识别"
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return f"识别主题 {len(items)} 条，涉及主题：{topic_text}；关键对象：{entity_text}"
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details_text = summarize_extracted_items(extracted_items)
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loading_prompts = [
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{"title": "📄 生成分析报告文本", "details": details_text},
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{"title": "✍️ 正在撰写分析报告", "details": details_text},
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{"title": "📋 生成历史问答分析报告", "details": details_text},
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{"title": "📝 构建分析报告结构", "details": details_text},
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{"title": "🎯 汇总历史问答数据生成报告", "details": details_text},
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]
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selected_prompt = random.choice(loading_prompts)
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start_event = {
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"type": "function_execution",
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"title": selected_prompt["title"],
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"details": selected_prompt["details"]
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}
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for callback in get_all_callbacks():
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try:
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callback(start_event)
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except Exception:
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pass
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history_context = build_history_str(history_messages, recent_count=10, assistant_truncate=300)
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if not history_context and history_message:
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history_context = history_message
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if not history_context and not combined_query:
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return {
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"report_text": "",
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"response": "【无法生成分析报告】\n\n缺少历史对话信息和用户输入，请先进行相关问答。",
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"actions": [],
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"route_flag": "report",
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"suggestedReplies": [],
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"error_message": "缺少历史对话信息和用户输入"
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}
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random_letters = ''.join(random.choices('ABCDEFGHIJKLMNOPQRSTUVWXYZ', k=2))
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report_number = time.strftime(f"{random_letters}-%Y-%m-%d-%H%M")
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compile_time = time.strftime("%Y-%m-%d %H:%M")
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image_section = ""
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if extracted_images:
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image_parts = []
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for idx, img in enumerate(extracted_images, 1):
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image_parts.append(f"图片{idx}：{img['url']}")
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summary = img.get('summary') or img.get('description', '')
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if summary:
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image_parts.append(f"识别结果：{summary}")
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image_parts.append("")
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image_section = "\n".join(image_parts)
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prompt = f"""
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<role>
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你是一名设备运维与故障分析报告编写专家。
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你的任务是根据用户需求、历史对话和抽取信息生成一份正式的故障处理报告。
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</role>
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<user_requirement>
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{combined_query}
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</user_requirement>
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<extracted_items>
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{extracted_items}
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</extracted_items>
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<history_context>
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{history_context if history_context else "（无历史对话上下文）"}
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</history_context>
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{f'''<user_images>
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{image_section}
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</user_images>''' if extracted_images else ''}
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<output_rules>
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必须严格遵守以下规则：
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1. 输出为 **正式技术报告文本**
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2. **使用段落形式，如果有表格生成，生成表格必须arkdown表格，不要html形式**
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3. **如果给定结果在历史对话信息中没有，可以进行删除**
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4. **输出内容必须严格按照历史对话信息内容进行生成，不得编造不实信息和不相干信息**
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5. 不得输出解释说明
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6. 不要使用列表符号
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7. 所有内容使用完整中文句子。
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8. **每个段落首行必须缩进，使用两个全角空格（ ）实现首行缩进两个中文字符的效果**
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{"9. **报告中必须包含用户上传的图片信息，在故障信息部分引用图片URL**" if extracted_images else ""}
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</output_rules>
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<report_template>
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{report_title}
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报告编号： {report_number}
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编制时间： {compile_time}
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事件类型： 设备故障诊断与处置
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一、事件概述
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简要说明故障发生背景、报警情况、系统影响以及运维团队启动排查流程的情况。
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二、故障信息
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说明设备名称、型号、所属系统、报警时间、故障现象以及参与处置人员。
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{" **附件图片：**" + chr(10) + chr(10).join([f" " for idx, img in enumerate(extracted_images, 1)]) if extracted_images else ""}
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三、故障排查过程
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按照时间顺序描述排查步骤，包括：用户反馈 → 初步判断 → 数据分析 → 现场检查 → 故障确认。
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四、维修措施
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描述维修人员采取的具体维修步骤，包括设备停机、拆卸检查、更换部件、紧固连接、恢复运行和测试过程。
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五、后续建议
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提出预防类似故障的改进措施，例如：维护周期优化、知识库记录、巡检项目增加、技术培训等。
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六、总结
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总结本次故障原因、处理效果以及经验意义。
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</report_template>
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<final_rule>
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只输出完整报告正文，不要任何解释说明。
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</final_rule>
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"""
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try:
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report_text = await OpenaiAPI.open_api_chat_without_thinking(
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query=prompt,
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model=None,
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messages=history_messages
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)
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if not report_text:
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report_text = "抱歉，当前无法生成分析报告，请稍后重试。"
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word_dir = os.path.join(parent_dir, "created_word")
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os.makedirs(word_dir, exist_ok=True)
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safe_topic = re.sub(r'[\\/:*?"<>|]', '_', report_title)
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file_name = f"{safe_topic}.docx"
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output_path = os.path.join(word_dir, file_name)
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word_result = generate_report_word(
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markdown=report_text,
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title=report_title,
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output=output_path
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)
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actions: List[Dict[str, Any]] = []
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if word_result and os.path.exists(word_result):
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base_url = SERVER_CONFIG.get("report")
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download_url = f"http://192.168.1.164:9088/api/download/{file_name}"
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actions.append({
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"type": "download",
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"label": f"{report_title}.docx",
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"url": download_url,
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"fileName": file_name,
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"content": report_text
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})
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else:
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error_msg = f"\n\n[注意] Word 文档生成失败"
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report_text += error_msg
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# actions.append({
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# "type": "regenerate",
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# "label": "重新生成",
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# "icon": "report"
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# })
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suggested_replies = await generate_suggested_replies_qa_report(report_text)
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return {
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"report_text": report_text,
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"response": report_text,
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"actions": actions,
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"route_flag": "report",
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"suggestedReplies": [],
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# "suggestedReplies": suggested_replies,
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"__details__": "分析报告生成完成"
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}
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except Exception as e:
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# error_event = {
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# "type": "function_execution",
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# "title": "步骤二失败：生成分析报告",
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# "details": f"错误: {str(e)}",
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# "has_result": False
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# }
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# for callback in get_all_callbacks():
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# try:
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# callback(error_event)
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# except Exception:
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# pass
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return {
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"report_text": f"生成分析报告失败: {str(e)}",
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"response": f"生成分析报告失败: {str(e)}",
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"actions": [],
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"route_flag": "report",
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"suggestedReplies": [],
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"error_message": str(e),
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"__details__": f"生成分析报告失败: {str(e)}"
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}
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async def generate_suggested_replies_qa_report(report: str) -> List[Dict[str, Any]]:
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"""
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生成针对历史问答分析报告的建议回复问题
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使用大模型生成两个相关问题
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"""
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prompt = f"""
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你是一个专业的数据分析助手。根据以下历史问答分析报告，生成两个用户可能关心的后续问题。
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【分析报告】
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{report[:500]}
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请生成两个简洁、实用的问题，这些问题应该：
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1. 不得出现需要归档和下载等操作相关的问题
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2. 与分析报告相关，能够帮助用户更好地理解或使用分析结果
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3. 问题要具体、可操作
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4. 每个问题不超过20个字
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请以JSON格式输出，格式如下：
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[
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{{"title": "问题1的标题", "content": "问题1的完整内容"}},
|
||
{{"title": "问题2的标题", "content": "问题2的完整内容"}}
|
||
]
|
||
|
||
只输出JSON，不要有其他文字说明。
|
||
"""
|
||
try:
|
||
answer = await OpenaiAPI.open_api_chat_without_thinking(
|
||
query=prompt,
|
||
model=None
|
||
)
|
||
json_match = re.search(r'\[.*\]', answer, re.DOTALL)
|
||
if json_match:
|
||
json_str = json_match.group(0)
|
||
suggested_replies = json.loads(json_str)
|
||
if isinstance(suggested_replies, list) and len(suggested_replies) >= 2:
|
||
formatted_replies = []
|
||
for reply in suggested_replies[:2]:
|
||
if isinstance(reply, dict) and "title" in reply and "content" in reply:
|
||
formatted_replies.append({
|
||
"title": str(reply["title"]),
|
||
"content": str(reply["content"])
|
||
})
|
||
if len(formatted_replies) == 2:
|
||
return formatted_replies
|
||
|
||
return [
|
||
{"title": "如何优化问题分布？", "content": "如何优化问题分布？"},
|
||
{"title": "有哪些改进建议？", "content": "有哪些改进建议？"}
|
||
]
|
||
except Exception as e:
|
||
return [
|
||
{"title": "如何优化问题分布？", "content": "如何优化问题分布？"},
|
||
{"title": "有哪些改进建议？", "content": "有哪些改进建议？"}
|
||
]
|
||
|
||
|
||
def create_history_qa_analysis_workflow():
|
||
graph = StateGraph(HistoryQAAnalysisState)
|
||
|
||
graph.add_node("extract_items", extract_analysis_items)
|
||
graph.add_node("generate_text", generate_analysis_report_text)
|
||
|
||
graph.add_edge(START, "extract_items")
|
||
graph.add_edge("extract_items", "generate_text")
|
||
graph.add_edge("generate_text", END)
|
||
|
||
return graph.compile()
|
||
|
||
|
||
# @track_function_calls
|
||
async def run_qa_report_agent(
|
||
extracted_text: str,
|
||
combined_query: str,
|
||
history_message: str = "",
|
||
route_flag: str = "",
|
||
extracted_images: List[Dict[str, str]] = None
|
||
) -> Dict[str, Any]:
|
||
app = create_history_qa_analysis_workflow()
|
||
|
||
initial_state: HistoryQAAnalysisState = {
|
||
"extracted_text": extracted_text,
|
||
"combined_query": combined_query,
|
||
**init_history(history_message),
|
||
"extracted_items": [],
|
||
"extracted_images": extracted_images or [],
|
||
"report_title": "",
|
||
"report_text": "",
|
||
"response": "",
|
||
"actions": [],
|
||
"route_flag": "",
|
||
"suggestedReplies": [],
|
||
"error_message": ""
|
||
}
|
||
|
||
return await app.ainvoke(initial_state)
|
||
|
||
|
||
if __name__ == "__main__":
|
||
import asyncio
|
||
|
||
|
||
async def test():
|
||
history_message = json.dumps(
|
||
[
|
||
{"role": "user", "content": "我需要分析最近的历史问答数据"},
|
||
{"role": "assistant", "content": "好的，我可以帮您生成历史问答分析报告，请提供相关数据。"},
|
||
{"role": "user", "content": "请重点关注设备故障和维护相关的内容"}
|
||
],
|
||
ensure_ascii=False,
|
||
)
|
||
|
||
result = await run_qa_report_agent(
|
||
extracted_text="请重点关注设备故障和维护相关的内容",
|
||
combined_query="用户问题：请重点关注设备故障和维护相关的内容",
|
||
history_message=history_message
|
||
)
|
||
|
||
print("====== 历史问答分析报告文本 ======")
|
||
print(result["response"])
|
||
print("\n====== 推荐问题 ======")
|
||
for reply in result.get("suggestedReplies", []):
|
||
print(f"- {reply['title']}: {reply['content']}")
|
||
print("\n====== 操作按钮 ======")
|
||
for action in result.get("actions", []):
|
||
print(f"- {action.get('label', 'Unknown')}: {action.get('type', 'Unknown')}")
|
||
|
||
|
||
asyncio.run(test())
|
||
|
||
|
||
|