岗位自动投递agent 对话功能
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"""求职助手 Agent 对话 AI 模块"""
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"""求职助手 Agent 对话 AI 引擎
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构造 prompt + 调 LLM + 解析返回。
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依赖：LLM 枚举、job_agent/prompts、parse_llm_json
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"""
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from app.ai.job_agent.prompts import SYSTEM_PROMPT
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from app.ai.models import LLM
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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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async def agent_chat(resume_text: str, message: str, history: list[dict],
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job_categories: list[str], regions: list[str],
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industries: list[str]) -> dict:
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"""求职助手对话
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1. 构造 system prompt 2. 拼 messages 3. 调 LLM 4. 解析返回
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"""
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# 1. 构造 system prompt
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system_content = SYSTEM_PROMPT.format(
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resume_text=resume_text,
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job_categories="、".join(job_categories) if job_categories else "未设置",
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regions="、".join(regions) if regions else "未设置",
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industries="、".join(industries) if industries else "未设置",
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)
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# 2. 拼 messages
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messages = [("system", system_content)]
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for msg in history:
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messages.append((msg["role"], msg["content"]))
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messages.append(("human", message))
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# 3. 调 LLM
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try:
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llm = LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.7)
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result = await llm.ainvoke(messages)
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raw = result.content
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except Exception as e:
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log.error(f"求职助手AI调用失败: {e}")
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return {"message": "抱歉，我暂时无法回复，请稍后再试。", "tool": None, "toolParams": None}
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# 4. 解析返回
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try:
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parsed = parse_llm_json(raw)
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return {
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"message": parsed.get("message", ""),
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"tool": parsed.get("tool"),
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"toolParams": parsed.get("toolParams"),
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}
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except Exception as e:
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log.warning(f"求职助手AI返回解析失败, raw={raw}, error={e}")
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return {"message": raw.strip() if raw else "抱歉，我暂时无法回复。", "tool": None, "toolParams": None}
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"""求职助手 Agent 对话 Prompt 模板"""
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SYSTEM_PROMPT = """你是 OfferPie 求职助手，帮助用户找到合适的工作。
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【用户简历】
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{resume_text}
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【求职意向】
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意向岗位：{job_categories}
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意向城市：{regions}
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意向行业：{industries}
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【你的能力】
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1. 回答求职相关问题（面试技巧、简历建议、行业分析等），回复不超过200字
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2. 当用户想看岗位推荐时，提取用户的偏好描述，调用岗位推荐工具
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3. 当用户想修改求职偏好/设置时，调用偏好设置工具
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【输出格式】
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严格返回 JSON，不要其他内容：
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{{"message":"回复内容","tool":null,"toolParams":null}}
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tool 可选值：
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- null：普通对话，不触发工具
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- "recommend"：岗位推荐，toolParams 必须包含 {{"preference":"用户偏好描述"}}
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- "editPreference"：调整偏好，toolParams 为 null
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【规则】
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1. 只聊求职相关话题，其他话题礼貌拒绝
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2. 回复简洁，不超过200字
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3. 用户表达想看岗位、推荐岗位、帮我找工作等意图时，从对话中提取偏好描述，返回 recommend
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4. 用户表达想改设置、调整偏好、修改意向等意图时，返回 editPreference
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5. 偏好描述要准确概括用户的岗位偏好，如"更偏技术方向的产品岗"、"大厂优先"
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6. 如果用户没有明确偏好，preference 填"无特殊偏好"
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"""
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"""求职助手 Agent 对话接口"""
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from fastapi import APIRouter
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from app.core.context import RequestContext
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from app.core.database import get_db
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from app.schemas.job_agent_chat import JobAgentChatParam
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from app.services.job_agent_chat_service import JobAgentChatService
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router = APIRouter(prefix="/job-agent", tags=["求职助手agent"])
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@router.post("/chat", summary="求职助手对话")
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async def chat(param: JobAgentChatParam):
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"""求职助手对话，根据用户简历和意向提供求职建议、触发岗位推荐或偏好调整"""
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user_id = RequestContext.user_id.get()
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async for session in get_db():
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service = JobAgentChatService(session)
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result = await service.chat(
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user_id, param.resume_id, param.message,
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[msg.model_dump() for msg in param.history],
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param.job_categories, param.regions, param.industries,
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)
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return result
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@@ -34,11 +34,13 @@ from app.api.health import router as health_router
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from app.api.resume import router as resume_router
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from app.api.resume_diagnose import router as resume_diagnose_router
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from app.api.skill_gap import router as skill_gap_router
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from app.api.job_agent_chat import router as job_agent_chat_router
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app.include_router(health_router)
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app.include_router(resume_router)
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app.include_router(resume_diagnose_router)
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app.include_router(skill_gap_router)
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app.include_router(job_agent_chat_router)
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# ==============================
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if __name__ == "__main__":
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"""求职助手 Agent 对话 Schema
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请求参数 Param、响应 Dto。
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字段命名使用 camelCase alias，与前端 JSON 对齐。
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"""
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from typing import Literal
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from pydantic import BaseModel, Field
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ChatRole = Literal["user", "assistant"]
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class ChatMessage(BaseModel):
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role: ChatRole
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content: str
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class JobAgentChatParam(BaseModel):
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message: str = Field(..., description="用户输入的消息")
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resume_id: int = Field(..., alias="resumeId", description="简历ID")
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history: list[ChatMessage] = Field(default_factory=list, description="对话历史")
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job_categories: list[str] = Field(default_factory=list, alias="jobCategories", description="意向岗位类型名称")
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regions: list[str] = Field(default_factory=list, alias="regions", description="意向城市名称")
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industries: list[str] = Field(default_factory=list, alias="industries", description="意向行业名称")
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class ToolParams(BaseModel):
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preference: str = Field(default="", description="用户岗位偏好描述")
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class JobAgentChatDto(BaseModel):
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message: str = Field(..., description="AI回复文本，不超过200字")
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tool: str | None = Field(default=None, description="前端需执行的工具：recommend / editPreference / null")
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tool_params: ToolParams | None = Field(default=None, alias="toolParams", description="工具参数")
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model_config = {"populate_by_name": True}
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"""求职助手 Agent 对话 Service
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主要功能：查询简历数据，调用 AI 模块完成对话。
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依赖：UserResume + 5张子表 ORM、job_agent.chat AI 模块
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使用表：bg_user_resume、bg_user_resume_education/work/internship/project/competition（查询简历）
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"""
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from sqlalchemy import select
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from sqlalchemy.ext.asyncio import AsyncSession
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from app.ai.job_agent.chat import agent_chat
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from app.models.user_resume import UserResume
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from app.models.user_resume_education import UserResumeEducation
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from app.models.user_resume_work import UserResumeWork
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from app.models.user_resume_internship import UserResumeInternship
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from app.models.user_resume_project import UserResumeProject
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from app.models.user_resume_competition import UserResumeCompetition
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class JobAgentChatService:
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def __init__(self, session: AsyncSession):
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self.session = session
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async def chat(self, user_id: int, resume_id: int, message: str,
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history: list[dict], job_categories: list[str],
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regions: list[str], industries: list[str]) -> dict:
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"""求职助手对话：查简历 → 序列化 → 调 AI 模块"""
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resume = await self._get_resume(resume_id, user_id)
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edu, work, intern, proj, comp = await self._load_sub_tables(resume_id)
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resume_text = self._build_resume_text(resume, edu, work, intern, proj, comp)
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return await agent_chat(resume_text, message, history, job_categories, regions, industries)
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async def _get_resume(self, resume_id: int, user_id: int) -> UserResume:
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"""查指定简历"""
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result = await self.session.execute(select(UserResume).where(UserResume.id == resume_id, UserResume.user_id == user_id))
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resume = result.scalar_one_or_none()
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if not resume:
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raise ValueError("简历不存在")
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return resume
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async def _load_sub_tables(self, resume_id: int):
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"""查询简历5张子表"""
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edu = (await self.session.execute(select(UserResumeEducation).where(UserResumeEducation.resume_id == resume_id))).scalars().all()
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work = (await self.session.execute(select(UserResumeWork).where(UserResumeWork.resume_id == resume_id))).scalars().all()
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intern = (await self.session.execute(select(UserResumeInternship).where(UserResumeInternship.resume_id == resume_id))).scalars().all()
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proj = (await self.session.execute(select(UserResumeProject).where(UserResumeProject.resume_id == resume_id))).scalars().all()
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comp = (await self.session.execute(select(UserResumeCompetition).where(UserResumeCompetition.resume_id == resume_id))).scalars().all()
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return edu, work, intern, proj, comp
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@staticmethod
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def _build_resume_text(resume: UserResume, edu_list, work_list, intern_list, proj_list, comp_list) -> str:
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"""将简历数据序列化为文本供 AI 使用"""
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parts = []
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if resume.name:
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parts.append(f"姓名：{resume.name}")
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if resume.target_position:
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parts.append(f"目标岗位：{resume.target_position}")
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if resume.skills:
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parts.append(f"技能：{'、'.join(resume.skills)}")
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if resume.certificates:
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parts.append(f"证书：{'、'.join(resume.certificates)}")
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if resume.summary:
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parts.append(f"个人概述：{resume.summary}")
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if edu_list:
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parts.append("教育经历：")
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for r in edu_list:
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parts.append(f" - {r.school or ''} {r.major or ''} {r.degree or ''}")
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if work_list:
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parts.append("工作经历：")
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for r in work_list:
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parts.append(f" - {r.company_name or ''} {r.position or ''}")
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if intern_list:
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parts.append("实习经历：")
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for r in intern_list:
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parts.append(f" - {r.company_name or ''} {r.position or ''}")
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if proj_list:
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parts.append("项目经历：")
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for r in proj_list:
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parts.append(f" - {r.project_name or ''} {r.role or ''}")
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if comp_list:
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parts.append("竞赛经历：")
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for r in comp_list:
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parts.append(f" - {r.competition_name or ''} {r.award or ''}")
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return "\n".join(parts) if parts else "暂无简历信息"
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