zk
52 lines
1.9 KiB
Python
52 lines
1.9 KiB
Python
"""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.model_config import JobAgentModel
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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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result = await JobAgentModel.CHAT.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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