wangwei
91c0dab4f9
- llm_analyzer.py: use llm.langchain_llm.ainvoke() (correct RAGAS 0.4.3 API) - webapp/models.py: add advice_markdown field to ReportData - webapp/services/run_reader.py: add read_advice_markdown() reading optimization_advice.md - webapp/services/report_builder.py: pass advice_markdown into ReportData - .env.example: OPENAI_TIMEOUT_SECONDS 30→180, RAGAS_METRIC_TIMEOUT_SECONDS 45→300 Co-Authored-By: Claude <noreply@anthropic.com>
101 lines
3.5 KiB
Python
101 lines
3.5 KiB
Python
"""LLM-powered analysis of rule diagnostics and low-score samples."""
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from __future__ import annotations
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import logging
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from typing import Any
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from .rules import Diagnosis
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logger = logging.getLogger("rag_eval.advisor")
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_PROMPT_TEMPLATE = """\
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你是一个 RAG 系统优化专家，正在分析西门子医疗 CT 文档问答系统的评测结果。
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请用中文撰写一份优化建议报告，格式为 Markdown。
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## 评测诊断摘要
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{diagnosis_summary}
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## 低分样本示例
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{low_sample_text}
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## 报告要求
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1. 按指标分节（## 指标名 [severity]），先解释"为什么低"（结合低分样本具体分析），再给出"具体怎么改"
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2. "具体怎么改"要结合低分样本的实际内容，而不只是泛泛建议
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3. 最后写一节 **## 优先优化次序**，按性价比排序（不增加 LLM 调用次数的优化优先）
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4. 语言简洁，面向工程师，不要废话，不要重复列表内容
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只输出 Markdown 报告正文，不要任何前置说明。
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"""
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def _build_diagnosis_summary(diagnoses: list[Diagnosis]) -> str:
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lines = []
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for d in diagnoses:
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direction = "（越低越好）" if d.metric == "noise_sensitivity" else ""
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lines.append(
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f"- **{d.metric}** {direction} 均值={d.mean_score:.4f}，"
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f"阈值={d.threshold}，严重程度={d.severity}"
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)
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lines.append(f" - 可能原因：{'; '.join(d.root_causes)}")
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lines.append(f" - 建议动作：{'; '.join(d.suggested_actions)}")
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return "\n".join(lines)
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def _build_low_sample_text(diagnoses: list[Diagnosis]) -> str:
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lines = []
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for d in diagnoses:
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if not d.low_samples:
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continue
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lines.append(f"### {d.metric} 低分样本（最多 3 条）")
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for i, s in enumerate(d.low_samples, 1):
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score = s.get(d.metric, "N/A")
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lines.append(f"\n**样本 {i}**（分数={score}）")
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lines.append(f"- 问题：{s.get('question', '')}")
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lines.append(f"- 回答：{s.get('answer', '')[:300]}")
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lines.append(f"- 标准答案：{s.get('ground_truth', '')[:200]}")
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return "\n".join(lines)
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async def analyze(
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diagnoses: list[Diagnosis],
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llm: Any,
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scenario_name: str,
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) -> str:
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"""Call the judge LLM to generate a Chinese optimization report.
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Args:
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diagnoses: Non-empty list of Diagnosis from rules.diagnose().
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llm: RAGAS LLM wrapper (has .agenerate() method).
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scenario_name: Used only for logging.
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Returns:
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LLM-generated Markdown string, or "" on failure (triggers writer fallback).
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"""
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if not diagnoses:
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return ""
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diagnosis_summary = _build_diagnosis_summary(diagnoses)
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low_sample_text = _build_low_sample_text(diagnoses)
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prompt = _PROMPT_TEMPLATE.format(
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diagnosis_summary=diagnosis_summary,
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low_sample_text=low_sample_text,
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)
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try:
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logger.info("[advisor] calling LLM for optimization analysis scenario=%s", scenario_name)
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from langchain_core.messages import HumanMessage
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# Use the underlying langchain chat model directly (RAGAS LangchainLLMWrapper wraps BaseChatModel)
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response = await llm.langchain_llm.ainvoke([HumanMessage(content=prompt)])
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text = response.content.strip()
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logger.info("[advisor] LLM analysis complete chars=%d", len(text))
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return text
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except Exception as exc:
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logger.warning(
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"[advisor] LLM analysis failed (%s: %s) — falling back to rule report",
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type(exc).__name__, exc,
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)
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return ""
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