zk
114 lines
4.0 KiB
Python
114 lines
4.0 KiB
Python
"""简历诊断 AI 引擎：并行诊断 + 汇总评价"""
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import asyncio
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from langchain_core.output_parsers import StrOutputParser
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from langchain_core.prompts import ChatPromptTemplate
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from app.ai.model_config import DiagnoserModel
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from app.ai.resume_diagnoser.prompts import DIAGNOSE_MODULE_PROMPT, SUMMARY_PROMPT, POLISH_PROMPT
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from app.core.logger import log
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from app.tool.json_helper import parse_llm_json
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# 诊断链（StrOutputParser 拿原始文本，再手动解析 JSON，避免 markdown 代码块导致解析失败）
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_diagnose_chain = (
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ChatPromptTemplate.from_messages([("system", DIAGNOSE_MODULE_PROMPT), ("human", "请开始诊断。")])
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| DiagnoserModel.MODULE
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| StrOutputParser()
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)
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# 汇总评价链（纯文本输出）
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_summary_chain = (
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ChatPromptTemplate.from_messages([("system", SUMMARY_PROMPT), ("human", "请生成整体评价。")])
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| DiagnoserModel.SUMMARY
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| StrOutputParser()
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)
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async def diagnose_all(tasks: list[dict]) -> list[dict]:
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"""并行诊断所有模块记录
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tasks: [{"module_type": ..., "target_position": ..., "context": ..., "description_text": ...}, ...]
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返回: 与 tasks 一一对应的诊断结果列表
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"""
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log.info(f"开始{len(tasks)}路并行AI诊断")
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results = await asyncio.gather(*[_safe_invoke(task) for task in tasks])
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log.info("并行AI诊断完成")
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return results
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async def generate_summary(grade: str, urgent_total: int, important_total: int,
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expression_total: int, target_position: str, all_findings: str) -> str:
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"""AI 生成整体评价文本"""
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inp = {
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"grade": grade, "urgent_total": str(urgent_total),
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"important_total": str(important_total), "expression_total": str(expression_total),
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"target_position": target_position or "未指定", "all_findings": all_findings,
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}
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try:
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return await _summary_chain.ainvoke(inp)
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except Exception as e:
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log.warning(f"AI生成整体评价失败: {e}")
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return "简历诊断已完成，请查看各模块的详细诊断结果。"
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_polish_chain = (
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ChatPromptTemplate.from_messages([("system", POLISH_PROMPT), ("human", "请开始优化。")])
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| DiagnoserModel.POLISH
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| StrOutputParser()
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)
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async def polish_content(module_type: str, reference_content: list[dict] | str | None,
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user_content: list[str], is_summary: bool) -> list[str]:
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"""润色用户编辑后的文本"""
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ref_text = ""
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if reference_content:
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if isinstance(reference_content, list):
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ref_text = "\n".join(
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item.get("text", "") if isinstance(item, dict) else str(item)
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for item in reference_content
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)
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else:
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ref_text = str(reference_content)
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if not ref_text:
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ref_text = "无"
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inp = {
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"module_type": module_type,
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"reference_content": ref_text,
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"user_content": "\n".join(user_content),
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"summary_constraint": "- 注意：此模块只能输出一个段落，数组只能有一个元素" if is_summary else "",
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}
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try:
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raw = await _polish_chain.ainvoke(inp)
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result = parse_llm_json(raw)
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if isinstance(result, list):
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return [str(item) for item in result]
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return [str(result)]
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except Exception as e:
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log.warning(f"AI润色失败: {e}")
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return user_content
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async def _safe_invoke(task: dict) -> dict:
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"""单条记录诊断，失败返回空结果"""
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raw = ""
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try:
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raw = await _diagnose_chain.ainvoke(task)
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return parse_llm_json(raw)
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except Exception as e:
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log.warning(f"AI诊断[{task.get('module_type', '')}]失败: {e}\n原始输出: {raw[:500]}")
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return _empty_result()
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def _empty_result() -> dict:
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return {
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"finding": "", "importance": "", "suggestion": "",
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"urgent_issues": {"typo": 0},
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"important_issues": {"no_result": 0, "no_quantify": 0, "weak_relevance": 0},
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"expression_issues": {"not_concise": 0, "format_inconsistent": 0},
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"optimized_content": None,
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}
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