feat: jc 引擎主线搬入 app/engine（315 测试基线全绿：320 去掉 admin 5 用例）

Co-Authored-By: Claude Code <noreply@anthropic.com>

app/engine/services/ai_service.py

@@ -0,0 +1,98 @@
+import json
+import logging
+from collections.abc import AsyncGenerator
+from pathlib import Path
+
+from app.engine.providers.base import AIProvider
+from app.engine.providers.glm import GLMProvider
+from app.engine.config import settings
+
+logger = logging.getLogger(__name__)
+
+PROMPTS_DIR = Path(__file__).resolve().parent.parent / "prompts"
+
+
+def _load_prompt(filename: str) -> str:
+    return (PROMPTS_DIR / filename).read_text(encoding="utf-8")
+
+
+def create_provider() -> AIProvider:
+    if settings.AI_PROVIDER == "glm":
+        return GLMProvider(
+            api_key=settings.ZHIPU_API_KEY,
+            model=settings.AI_MODEL,
+            base_url=settings.ZHIPU_BASE_URL,
+        )
+    raise ValueError(f"Unknown AI provider: {settings.AI_PROVIDER}")
+
+
+SYSTEM_PROMPT = _load_prompt("system.md")
+COMPANY_FACTS = _load_prompt("company_facts.md")   # 公司事实卡(知识与行为分离)
+EXTRACTOR_PROMPT = _load_prompt("extractor.md")
+EVALUATOR_PROMPT = _load_prompt("evaluator.md")
+
+# 对话基座: 行为 prompt 在前(红线显著性),事实卡居中(参考资料),
+# 当轮 context 提醒最后拼接(在 chat/stream_chat 内,保持最新鲜)
+_BASE_CHAT_PROMPT = SYSTEM_PROMPT + "\n\n" + COMPANY_FACTS
+
+
+class AIService:
+    def __init__(self, provider: AIProvider | None = None):
+        self.provider = provider or create_provider()
+
+    async def chat(self, messages: list[dict], context: str = "") -> str:
+        """Main conversation: get AI reply to user message."""
+        prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
+        return await self.provider.chat(messages, prompt)
+
+    async def stream_chat(self, messages: list[dict], context: str = "") -> AsyncGenerator[str, None]:
+        """Stream AI reply token by token."""
+        prompt = _BASE_CHAT_PROMPT + ("\n\n" + context if context else "")
+        async for token in self.provider.stream_chat(messages, prompt):
+            yield token
+
+    async def extract_info(
+        self, messages: list[dict], existing_info: dict
+    ) -> dict:
+        """Extract structured info from conversation history."""
+        prompt = EXTRACTOR_PROMPT.replace(
+            "$existing_info", json.dumps(existing_info or {}, ensure_ascii=False)
+        ).replace("$messages", self._format_messages(messages))
+        _, extracted = await self.provider.chat_with_extraction(
+            messages=[], system_prompt=prompt
+        )
+        return extracted
+
+    async def evaluate(
+        self, profile: dict, recent_messages: list[dict]
+    ) -> dict:
+        """Evaluate customer intent level."""
+        prompt = EVALUATOR_PROMPT.replace(
+            "$profile", json.dumps(profile, ensure_ascii=False)
+        ).replace("$recent_messages", self._format_messages(recent_messages[-6:]))
+        _, result = await self.provider.chat_with_extraction(
+            messages=[], system_prompt=prompt
+        )
+        return self._normalize_evaluation(result)
+
+    @staticmethod
+    def _format_messages(messages: list[dict]) -> str:
+        lines = []
+        for m in messages:
+            role = m.get("role", "unknown")
+            content = m.get("content", "")
+            label = {"user": "客户", "assistant": "顾问"}.get(role, role)
+            lines.append(f"{label}: {content}")
+        return "\n".join(lines)
+
+    @staticmethod
+    def _normalize_evaluation(result: dict) -> dict:
+        intent = result.get("intent_level", "pending")
+        if intent not in ("interested", "not_interested", "pending"):
+            intent = "pending"
+        return {
+            "intent_level": intent,
+            "reasoning": result.get("reasoning", ""),
+            "contact_info": result.get("contact_info", {}),
+            "info_sufficient": result.get("info_sufficient", False),
+        }