trade-assistant/backend/app/api/v1/ai_assistant.py

from fastapi import APIRouter, Depends, HTTPException, Request
from pydantic import BaseModel
from typing import Optional, List, Dict, Any
from sqlalchemy.ext.asyncio import AsyncSession
from sqlalchemy import select
from app.database import get_db
from app.ai.router import get_ai_router
from app.ai.local_faq import match_faq
from app.api.v1.deps import get_current_user_id
from app.models.system_config import SystemConfig
from app.services.admin import AdminService
import logging
import time
import re
import json

logger = logging.getLogger(__name__)

router = APIRouter()


ACTION_INSTRUCTIONS = """
当用户想要执行操作时（如添加客户、创建产品、生成报价单、发送跟进、营销生成等），请执行以下步骤：
1. 从用户消息中提取所有必要的信息
2. 在回复末尾附上 JSON 格式的动作块，格式如下：

```actions
[{"type": "create_customer", "label": "添加客户", "fields": {"name": "...", "phone": "...", "email": "...", "company": "...", "country": "...", "notes": "..."}}]
```

支持的 action type 及字段说明：
- create_customer：添加客户，fields 支持 name(必填), phone, email, company, country, website, notes
- create_product：添加产品，fields 支持 name(必填), name_en, description, description_en, category, price, price_unit(默认USD), moq, keywords(逗号分隔)
- create_quotation：生成报价单，fields 支持 customer_name(必填), product_info(必填), quantity(必填), price, terms
- scan_followups：扫描待跟进客户，fields 不需要（空对象）
- send_followup：发送跟进消息，fields 支持 customer_name(必填), message(必填)
- generate_marketing：生成营销素材，fields 支持 product_name(必填), target_market, tone(如professional/casual), language
- discovery_search：搜索潜在客户，fields 支持 keywords(必填), country, industry
- navigate：跳转到指定页面，fields 支持 path(必填, 如 /customers /products /quotations /marketing /discovery /followup /translate /team /analytics)
- search_users：搜索用户，fields 支持 query(必填)
- update_user：修改用户信息，fields 支持 user_id(必填), username, phone, email, role, status
- update_config：更新系统配置，fields 支持 key(必填), value(必填)
- review_certification：审核认证，fields 支持 id(必填), action(approved/rejected), reason
- process_invoice：处理发票，fields 支持 id(必填), action(approve/reject)

如果用户没有提供足够信息，请先询问缺少的字段，不要生成 action。
如果用户明确表示要执行操作但缺少信息，生成 action 但标注缺失的字段。
"""


class ChatRequest(BaseModel):
    message: str
    history: Optional[List[Dict[str, str]]] = None


@router.get("/quick-questions")
async def get_quick_questions(
    db: AsyncSession = Depends(get_db),
):
    result = await db.execute(
        select(SystemConfig).where(SystemConfig.key == "ai_assistant_quick_questions")
    )
    cfg = result.scalar_one_or_none()
    return cfg.value if cfg and cfg.value else []


@router.post("/chat")
async def chat(
    data: ChatRequest,
    request: Request,
    user_id: str = Depends(get_current_user_id),
    db: AsyncSession = Depends(get_db),
):
    t_start = time.time()

    if not data.message.strip():
        raise HTTPException(status_code=422, detail="Message is required")

    t0 = time.time()
    prompt_config = await db.execute(
        select(SystemConfig).where(SystemConfig.key == "ai_assistant_prompt")
    )
    quick_config = await db.execute(
        select(SystemConfig).where(SystemConfig.key == "ai_assistant_quick_questions")
    )
    t1 = time.time()

    prompt_row = prompt_config.scalar_one_or_none()
    base_prompt = prompt_row.value if prompt_row else None
    quick_row = quick_config.scalar_one_or_none()
    quick_questions = quick_row.value if quick_row else None
    t2 = time.time()

    action_keywords = ["帮我添加", "帮我创建", "帮我新增", "帮我新建", "帮我录入", "帮.+加", "添加客户.*电话", "创建客户.*电话"]
    needs_action = any(re.search(k, data.message) for k in action_keywords)
    system_prompt = base_prompt or ""
    if needs_action:
        system_prompt += "\n\n" + ACTION_INSTRUCTIONS

    faq_answer = match_faq(data.message)
    if faq_answer and not needs_action:
        reply = faq_answer
        provider = "local-faq"
        actions = []
        t4 = time.time()
        logger.info(
            f"CHAT [{user_id[:8]}] FAQ match | "
            f"db={t1-t0:.2f}s orm={t2-t1:.2f}s total={t4-t_start:.2f}s"
        )
    else:
        t3 = time.time()
        ai = get_ai_router()
        result = await ai.chat(data.message, data.history or [], system_prompt)
        t4 = time.time()

        reply = result.get("reply", "")
        provider = result.get("provider_used", "")

        actions = []
        action_match = re.search(r'```actions\s*\n(.*?)\n```', reply, re.DOTALL)
        if action_match:
            try:
                actions = json.loads(action_match.group(1))
                reply = reply[:action_match.start()].strip()
            except (json.JSONDecodeError, Exception) as e:
                logger.warning(f"Failed to parse actions: {e}")

        logger.info(
            f"CHAT [{user_id[:8]}] AI | "
            f"db={t1-t0:.2f}s orm={t2-t1:.2f}s setup={t3-t2:.2f}s ai={t4-t3:.2f}s total={t4-t_start:.2f}s"
        )

    client_ip = request.client.host if request.client else None
    await AdminService(db).log_usage(
        user_id, "ai.chat",
        {"message": data.message[:200], "reply_length": len(reply), "provider": provider},
        ip=client_ip,
    )
    logger.info(f"CHAT [{user_id[:8]}] done total={time.time()-t_start:.2f}s")

    return {
        "reply": reply,
        "provider": provider,
        "quick_questions": quick_questions,
        "actions": actions,
    }