```

feat: 添加公司AI管理平台基础架构

添加了完整的FastAPI后端项目结构，包括：
- 环境配置文件(.env.example)和项目说明文档(README.md、AGENTS.md)
- Dockerfile用于容器化部署
- 核心基础设施：配置管理、数据库连接、调度器、安全认证
- 模块化设计：AI代理、审批流程、审计日志、业务台账等功能模块
- 支持多数据库连接(主库和遗留系统只读库)
- AI适配器支持OpenClaw、Hermes、OpenAI兼容接口
- 飞书集成、报表生成、风险监控等企业级功能
- 完整的依赖管理和测试指南
```

app/modules/ai_agent/service.py

@@ -0,0 +1,70 @@
+from typing import Any
+
+from sqlalchemy.orm import Session
+
+from app.modules.ai_agent.adapters import get_adapter
+from app.modules.audit.schemas import AuditLogCreate
+from app.modules.audit.service import AuditService
+
+
+class AIService:
+    """Coordinate AI provider calls and audit logging."""
+
+    def __init__(self, db: Session):
+        self.db = db
+        self.audit = AuditService(db)
+
+    def ask(
+        self,
+        prompt: str,
+        context: dict[str, Any] | None = None,
+        actor: str = "api",
+        source: str = "api",
+    ) -> dict[str, Any]:
+        adapter = get_adapter()
+        result = adapter.ask(prompt, context or {})
+        response = {
+            "provider": adapter.provider_name,
+            "answer": result["answer"],
+            "raw": result.get("raw", {}),
+        }
+        self.audit.log(
+            AuditLogCreate(
+                actor=actor,
+                source=source,
+                action="ai.ask",
+                target_type="ai",
+                risk_level="medium",
+                request_payload={"prompt": prompt, "context": context or {}},
+                response_payload=response,
+            )
+        )
+        return response
+
+    def draft_policy(
+        self,
+        title: str,
+        policy_type: str,
+        requirements: list[str],
+        actor: str,
+    ) -> dict[str, Any]:
+        prompt = (
+            f"Draft a company policy in Chinese. Title: {title}. Type: {policy_type}. "
+            "Include purpose, scope, roles, process, approval rules, audit rules, and KPI linkage. "
+            f"Requirements: {requirements}"
+        )
+        return self.ask(prompt, actor=actor, source="policy")
+
+    def draft_investment_research(
+        self,
+        symbol_or_topic: str,
+        risk_preference: str,
+        actor: str,
+    ) -> dict[str, Any]:
+        prompt = (
+            "Create an investment research memo in Chinese. Do not give direct trading "
+            "instructions. Include thesis, risks, data needed, position sizing constraints, "
+            "and human approval checklist. "
+            f"Topic: {symbol_or_topic}. Risk preference: {risk_preference}."
+        )
+        return self.ask(prompt, actor=actor, source="investment")