analysis_report_node.py
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"""
分析报告生成节点
在补货建议工作流的最后一个节点执行，生成结构化分析报告
"""
import logging
import time
import json
import os
from typing import Dict, Any
from decimal import Decimal
from datetime import datetime
from langchain_core.messages import HumanMessage
from ..llm import get_llm_client
from ..models import AnalysisReport
from ..services.result_writer import ResultWriter
logger = logging.getLogger(__name__)
def _load_prompt(filename: str) -> str:
"""从prompts目录加载提示词文件"""
prompts_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))),
"prompts"
)
filepath = os.path.join(prompts_dir, filename)
if not os.path.exists(filepath):
raise FileNotFoundError(f"Prompt文件未找到: {filepath}")
with open(filepath, "r", encoding="utf-8") as f:
return f.read()
def _calculate_risk_stats(part_ratios: list) -> dict:
"""计算风险统计数据"""
stats = {
"shortage_cnt": 0,
"shortage_amount": Decimal("0"),
"stagnant_cnt": 0,
"stagnant_amount": Decimal("0"),
"low_freq_cnt": 0,
"low_freq_amount": Decimal("0"),
}
for pr in part_ratios:
valid_storage = Decimal(str(pr.get("valid_storage_cnt", 0) or 0))
avg_sales = Decimal(str(pr.get("avg_sales_cnt", 0) or 0))
out_stock = Decimal(str(pr.get("out_stock_cnt", 0) or 0))
cost_price = Decimal(str(pr.get("cost_price", 0) or 0))
# 呆滞件: 有库存但90天无出库
if valid_storage > 0 and out_stock == 0:
stats["stagnant_cnt"] += 1
stats["stagnant_amount"] += valid_storage * cost_price
# 低频件: 无库存且月均销量<1
elif valid_storage == 0 and avg_sales < 1:
stats["low_freq_cnt"] += 1
# 缺货件: 无库存且月均销量>=1
elif valid_storage == 0 and avg_sales >= 1:
stats["shortage_cnt"] += 1
# 缺货损失估算：月均销量 * 成本价
stats["shortage_amount"] += avg_sales * cost_price
return stats
def _build_suggestion_summary(part_results: list, allocated_details: list) -> str:
"""构建补货建议汇总文本"""
if not part_results and not allocated_details:
return "暂无补货建议"
lines = []
total_cnt = 0
total_amount = Decimal("0")
# 优先使用 part_results (配件级汇总)
if part_results:
for pr in part_results[:10]: # 只取前10个
if hasattr(pr, "part_code"):
lines.append(
f"- {pr.part_code} {pr.part_name}: "
f"建议{pr.total_suggest_cnt}件, "
f"金额{pr.total_suggest_amount:.2f}元, "
f"优先级{pr.priority}"
)
total_cnt += pr.total_suggest_cnt
total_amount += pr.total_suggest_amount
elif isinstance(pr, dict):
lines.append(
f"- {pr.get('part_code', '')} {pr.get('part_name', '')}: "
f"建议{pr.get('total_suggest_cnt', 0)}件, "
f"金额{pr.get('total_suggest_amount', 0):.2f}元"
)
lines.insert(0, f"**总计**: {total_cnt}件配件, 金额{total_amount:.2f}元\n")
return "\n".join(lines)
def generate_analysis_report_node(state: dict) -> dict:
"""
生成分析报告节点
输入: part_ratios, llm_suggestions, allocated_details, part_results
输出: analysis_report
"""
start_time = time.time()
task_no = state.get("task_no", "")
group_id = state.get("group_id", 0)
dealer_grouping_id = state.get("dealer_grouping_id", 0)
dealer_grouping_name = state.get("dealer_grouping_name", "")
brand_grouping_id = state.get("brand_grouping_id")
statistics_date = state.get("statistics_date", "")
part_ratios = state.get("part_ratios", [])
part_results = state.get("part_results", [])
allocated_details = state.get("allocated_details", [])
logger.info(f"[{task_no}] 开始生成分析报告: dealer={dealer_grouping_name}")
try:
# 计算风险统计
risk_stats = _calculate_risk_stats(part_ratios)
# 构建建议汇总
suggestion_summary = _build_suggestion_summary(part_results, allocated_details)
# 加载 Prompt
prompt_template = _load_prompt("analysis_report.md")
# 填充 Prompt 变量
prompt = prompt_template.format(
dealer_grouping_id=dealer_grouping_id,
dealer_grouping_name=dealer_grouping_name,
statistics_date=statistics_date,
suggestion_summary=suggestion_summary,
shortage_cnt=risk_stats["shortage_cnt"],
shortage_amount=f"{risk_stats['shortage_amount']:.2f}",
stagnant_cnt=risk_stats["stagnant_cnt"],
stagnant_amount=f"{risk_stats['stagnant_amount']:.2f}",
low_freq_cnt=risk_stats["low_freq_cnt"],
low_freq_amount="0.00", # 低频件无库存
)
# 调用 LLM
llm_client = get_llm_client()
response = llm_client.invoke(
messages=[HumanMessage(content=prompt)],
)
# 解析 JSON 响应
response_text = response.content.strip()
# 移除可能的 markdown 代码块
if response_text.startswith("```"):
lines = response_text.split("\n")
response_text = "\n".join(lines[1:-1])
report_data = json.loads(response_text)
# 计算统计信息
total_suggest_cnt = sum(
d.suggest_cnt if hasattr(d, "suggest_cnt") else d.get("suggest_cnt", 0)
for d in allocated_details
)
total_suggest_amount = sum(
d.suggest_amount if hasattr(d, "suggest_amount") else Decimal(str(d.get("suggest_amount", 0)))
for d in allocated_details
)
execution_time_ms = int((time.time() - start_time) * 1000)
# 创建报告对象
# 新 prompt 字段名映射到现有数据库字段：
# overall_assessment -> replenishment_insights
# risk_alerts -> urgency_assessment
# procurement_strategy -> strategy_recommendations
# expected_impact -> expected_outcomes
# execution_guide 已移除，置为 None
report = AnalysisReport(
task_no=task_no,
group_id=group_id,
dealer_grouping_id=dealer_grouping_id,
dealer_grouping_name=dealer_grouping_name,
brand_grouping_id=brand_grouping_id,
report_type="replenishment",
replenishment_insights=report_data.get("overall_assessment"),
urgency_assessment=report_data.get("risk_alerts"),
strategy_recommendations=report_data.get("procurement_strategy"),
execution_guide=None,
expected_outcomes=report_data.get("expected_impact"),
total_suggest_cnt=total_suggest_cnt,
total_suggest_amount=total_suggest_amount,
shortage_risk_cnt=risk_stats["shortage_cnt"],
excess_risk_cnt=risk_stats["stagnant_cnt"],
stagnant_cnt=risk_stats["stagnant_cnt"],
low_freq_cnt=risk_stats["low_freq_cnt"],
llm_provider=getattr(llm_client, "provider", ""),
llm_model=getattr(llm_client, "model", ""),
llm_tokens=response.usage.total_tokens,
execution_time_ms=execution_time_ms,
statistics_date=statistics_date,
)
# 保存到数据库
result_writer = ResultWriter()
try:
result_writer.save_analysis_report(report)
finally:
result_writer.close()
logger.info(
f"[{task_no}] 分析报告生成完成: "
f"shortage={risk_stats['shortage_cnt']}, "
f"stagnant={risk_stats['stagnant_cnt']}, "
f"time={execution_time_ms}ms"
)
return {
"analysis_report": report.to_dict(),
"end_time": time.time(),
}
except Exception as e:
logger.error(f"[{task_no}] 分析报告生成失败: {e}", exc_info=True)
# 返回空报告，不中断整个流程
return {
"analysis_report": {
"error": str(e),
"task_no": task_no,
},
"end_time": time.time(),
}