Files
company-ai-platform/app/modules/ai_agent/service.py
JiuContinent 71ca804764 ```
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
```
2026-06-21 21:57:28 +08:00

71 lines
2.2 KiB
Python

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")