232 lines
7.6 KiB
Python
232 lines
7.6 KiB
Python
from __future__ import annotations
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import json
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import re
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from collections import Counter
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from typing import Protocol
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import requests
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from app.config import ProviderConfig
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from app.docx_parser import ParsedDocument
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from app.report_generator import AnalysisReport
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from app.skill_loader import Skill
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class SupportsPost(Protocol):
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def post(self, url: str, **kwargs): ...
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IMPORTANT_TERMS = {
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"需求",
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"接口",
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"测试",
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"合格性",
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"追踪",
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"追溯",
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"配置",
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"质量",
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"部署",
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"安装",
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"验收",
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"设计",
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"资源",
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"风险",
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"计划",
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"说明",
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"文档",
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"CSCI",
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"GJB",
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}
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def _tokens(text: str) -> list[str]:
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ascii_tokens = re.findall(r"[A-Za-z][A-Za-z0-9_-]{2,}", text)
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chinese_terms = [term for term in IMPORTANT_TERMS if term in text]
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return [token.lower() for token in ascii_tokens] + chinese_terms
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def select_relevant_skills(parsed: ParsedDocument, skills: list[Skill], max_skills: int = 6) -> list[Skill]:
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document_tokens = Counter(_tokens(parsed.text + "\n" + "\n".join(h.text for h in parsed.headings)))
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scored: list[tuple[int, Skill]] = []
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for skill in skills:
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skill_text = f"{skill.slug}\n{skill.name}\n{skill.description}\n{skill.use_when}\n{skill.content[:3000]}"
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score = 0
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skill_tokens = set(_tokens(skill_text))
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for token, count in document_tokens.items():
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if token in skill_tokens:
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score += count
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if parsed.filename.lower().endswith(".docx"):
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score += 1
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if score > 0:
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scored.append((score, skill))
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scored.sort(key=lambda item: (-item[0], item[1].slug))
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if not scored:
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return skills[:max_skills]
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return [skill for _, skill in scored[:max_skills]]
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def normalize_selected_skill_slugs(selected_slugs: list[str] | None, skills: list[Skill]) -> list[Skill]:
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if not selected_slugs:
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return skills
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available = {skill.slug: skill for skill in skills}
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picked = [available[slug] for slug in selected_slugs if slug in available]
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return picked or skills
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def build_analysis_prompt(parsed: ParsedDocument, skills: list[Skill]) -> str:
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skill_sections = []
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for skill in skills:
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skill_sections.append(
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f"## {skill.slug}\n名称: {skill.name}\n描述: {skill.description}\n适用条件: {skill.use_when}\n规范内容:\n{skill.content[:6000]}"
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)
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document_outline = "\n".join(f"- H{heading.level} {heading.text}" for heading in parsed.headings[:80]) or "未识别到标题。"
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document_text = parsed.text[:18000]
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return f"""你是军用软件文档符合性审查助手。请依据给定 GJB438C/GJB2786 技能规范，分析上传 DOCX 是否符合规范。
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请输出中文 Markdown，必须包含以下小节：
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1. 总体结论
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2. 符合项
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3. 不符合项
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4. 缺失章节或缺失证据
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5. 整改建议
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6. 需人工复核事项
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要求：
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- 每个问题尽量引用文档中的标题、关键词或证据摘要。
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- 不要编造未在文档中出现的证据。
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- 如果无法判断，标记为“需人工复核”。
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# 文件
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{parsed.filename}
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# 文档目录
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{document_outline}
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# 待检查技能
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{chr(10).join(skill_sections)}
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# 文档正文摘录
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{document_text}
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"""
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class LLMClient:
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def __init__(self, provider: ProviderConfig, session: SupportsPost | None = None, timeout: int = 120) -> None:
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self.provider = provider
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self.session = session or requests.Session()
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self.timeout = timeout
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def complete(self, prompt: str) -> str:
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headers = {"Content-Type": "application/json"}
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if self.provider.api_key and self.provider.api_key != "EMPTY":
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headers["Authorization"] = f"Bearer {self.provider.api_key}"
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payload = {
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"model": self.provider.model,
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"messages": [{"role": "user", "content": prompt}],
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"temperature": self.provider.temperature,
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"max_tokens": self.provider.max_tokens,
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}
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response = self.session.post(
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self.provider.chat_completions_url,
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headers=headers,
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json=payload,
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timeout=self.timeout,
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)
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response.raise_for_status()
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data = response.json()
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return data["choices"][0]["message"]["content"]
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def heuristic_analysis(parsed: ParsedDocument, skills: list[Skill]) -> AnalysisReport:
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headings_text = "\n".join(h.text for h in parsed.headings)
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full_text = parsed.text
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findings: list[dict[str, str]] = []
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recommendations: list[str] = []
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required_terms = ["范围", "引用文档", "需求", "合格性", "追踪", "接口"]
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for term in required_terms:
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present = term in full_text or term in headings_text
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findings.append(
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{
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"status": "符合" if present else "需整改",
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"item": f"检查关键内容：{term}",
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"evidence": "文档中已发现相关表述" if present else "未在解析文本中发现明确表述",
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}
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)
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if not present:
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recommendations.append(f"补充或明确“{term}”相关章节与证据。")
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if not parsed.headings:
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findings.append({"status": "需整改", "item": "章节结构", "evidence": "未识别到 Word 标题样式"})
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recommendations.append("使用 Word 标题样式组织章节，便于目录和符合性审查。")
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issue_count = sum(1 for item in findings if item["status"] != "符合")
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summary = "通过" if issue_count == 0 else "部分通过，需人工复核"
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raw_output = "未调用模型，已使用本地启发式规则生成初步分析。"
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return AnalysisReport(
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source_filename=parsed.filename,
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provider_name="local",
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model_name="heuristic",
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matched_skills=[skill.slug for skill in skills],
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summary=summary,
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findings=findings,
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recommendations=recommendations or ["保持现有章节结构，并由人工进行最终符合性确认。"],
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raw_model_output=raw_output,
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)
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def report_from_model_output(
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parsed: ParsedDocument,
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skills: list[Skill],
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provider_name: str,
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model_name: str,
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output: str,
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) -> AnalysisReport:
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findings = [{"status": "模型分析", "item": "完整分析结果", "evidence": output[:1200]}]
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recommendations = _extract_recommendations(output)
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return AnalysisReport(
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source_filename=parsed.filename,
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provider_name=provider_name,
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model_name=model_name,
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matched_skills=[skill.slug for skill in skills],
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summary=_extract_summary(output),
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findings=findings,
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recommendations=recommendations,
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raw_model_output=output,
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)
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def _extract_summary(output: str) -> str:
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for line in output.splitlines():
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normalized = line.strip(" #：:")
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if "总体结论" in normalized and len(normalized) > 4:
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return normalized
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return "模型已生成分析结果，需人工复核"
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def _extract_recommendations(output: str) -> list[str]:
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recommendations: list[str] = []
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in_section = False
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for line in output.splitlines():
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stripped = line.strip()
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if "整改建议" in stripped or "修改建议" in stripped:
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in_section = True
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continue
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if in_section and stripped.startswith("#"):
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break
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if in_section and stripped.lstrip("-0123456789.、 "):
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recommendations.append(stripped.lstrip("-0123456789.、 "))
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return recommendations[:10] or ["按模型分析结果逐项整改，并进行人工复核。"]
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def serialize_prompt_debug(prompt: str) -> str:
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return json.dumps({"prompt_preview": prompt[:2000]}, ensure_ascii=False, indent=2)
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