岗位自动投递agent 对话功能

app/ai/job_agent/chat.py

@@ -0,0 +1,52 @@
+"""求职助手 Agent 对话 AI 引擎
+
+构造 prompt + 调 LLM + 解析返回。
+依赖：LLM 枚举、job_agent/prompts、parse_llm_json
+"""
+
+from app.ai.job_agent.prompts import SYSTEM_PROMPT
+from app.ai.models import LLM
+from app.core.logger import log
+from app.tool.json_helper import parse_llm_json
+
+
+async def agent_chat(resume_text: str, message: str, history: list[dict],
+                     job_categories: list[str], regions: list[str],
+                     industries: list[str]) -> dict:
+    """求职助手对话
+    1. 构造 system prompt 2. 拼 messages 3. 调 LLM 4. 解析返回
+    """
+    # 1. 构造 system prompt
+    system_content = SYSTEM_PROMPT.format(
+        resume_text=resume_text,
+        job_categories="、".join(job_categories) if job_categories else "未设置",
+        regions="、".join(regions) if regions else "未设置",
+        industries="、".join(industries) if industries else "未设置",
+    )
+
+    # 2. 拼 messages
+    messages = [("system", system_content)]
+    for msg in history:
+        messages.append((msg["role"], msg["content"]))
+    messages.append(("human", message))
+
+    # 3. 调 LLM
+    try:
+        llm = LLM.JIAYU_CLAUDE_SONNET_4_5.create(temperature=0.7)
+        result = await llm.ainvoke(messages)
+        raw = result.content
+    except Exception as e:
+        log.error(f"求职助手AI调用失败: {e}")
+        return {"message": "抱歉，我暂时无法回复，请稍后再试。", "tool": None, "toolParams": None}
+
+    # 4. 解析返回
+    try:
+        parsed = parse_llm_json(raw)
+        return {
+            "message": parsed.get("message", ""),
+            "tool": parsed.get("tool"),
+            "toolParams": parsed.get("toolParams"),
+        }
+    except Exception as e:
+        log.warning(f"求职助手AI返回解析失败, raw={raw}, error={e}")
+        return {"message": raw.strip() if raw else "抱歉，我暂时无法回复。", "tool": None, "toolParams": None}