"""求职助手 - 岗位简历优化 AI 引擎

针对目标岗位并发优化简历（summary + 经历子表）。
依赖：LLM 枚举、job_agent/prompts、parse_llm_json
"""

from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate

from app.ai.job_agent.prompts import RESUME_SUMMARY_OPTIMIZE_PROMPT, RESUME_EXPERIENCE_OPTIMIZE_PROMPT
from app.ai.models import LLM
from app.core.logger import log
from app.tool.json_helper import parse_llm_json

# ===== summary 优化 =====

_summary_chain = (
    ChatPromptTemplate.from_messages([("system", RESUME_SUMMARY_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
    | LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
    | StrOutputParser()
)


async def optimize_summary(job_title: str, job_description: str, original_summary: str) -> str:
    """针对岗位优化个人概述"""
    try:
        return await _summary_chain.ainvoke({
            "job_title": job_title, "job_description": job_description or "",
            "original_summary": original_summary or "暂无",
        })
    except Exception as e:
        log.warning(f"岗位简历summary优化失败: {e}")
        return original_summary


# ===== 经历优化 =====

_experience_chain = (
    ChatPromptTemplate.from_messages([("system", RESUME_EXPERIENCE_OPTIMIZE_PROMPT), ("human", "请开始优化。")])
    | LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.3)
    | StrOutputParser()
)


async def optimize_experience(job_title: str, job_description: str, module_data: str) -> list | dict | None:
    """针对岗位优化经历模块描述，返回修改后的完整模块数据"""
    try:
        raw = await _experience_chain.ainvoke({
            "job_title": job_title, "job_description": job_description or "",
            "original_module_data": module_data,
        })
        return parse_llm_json(raw)
    except Exception as e:
        log.warning(f"岗位简历经历优化失败: {e}")
        return None