# backend/main.py
from fastapi import FastAPI, APIRouter, BackgroundTasks
from .service import crawler_sql_service
from .workflow import workflow
from .schemas import (
    RegisterRequest, PendingRequest, SaveResultsRequest, AddUrlsRequest, SearchRequest,
    AutoMapRequest, AutoProcessRequest, TextSearchRequest
)
from .utils import make_response

app = FastAPI(title="Wiki Crawler API")

# ==========================================
# V1 Router: 原始的底层接口 (Manual Control)
# ==========================================
router_v1 = APIRouter()

@router_v1.post("/register")
async def register(req: RegisterRequest):
    try:
        data = crawler_sql_service.register_task(req.url)
        return make_response(1, "Success", data)
    except Exception as e:
        return make_response(0, str(e))

@router_v1.post("/add_urls")
async def add_urls(req: AddUrlsRequest):
    try:
        urls = req.urls_obj["urls"]
        data = crawler_sql_service.add_urls(req.task_id, urls=urls)
        return make_response(1, "Success", data)
    except Exception as e:
        return make_response(0, str(e))

@router_v1.post("/pending_urls")
async def pending_urls(req: PendingRequest):
    try:
        data = crawler_sql_service.get_pending_urls(req.task_id, req.limit)
        msg = "Success" if data["urls"] else "Queue Empty"
        return make_response(1, msg, data)
    except Exception as e:
        return make_response(0, str(e))

@router_v1.post("/save_results")
async def save_results(req: SaveResultsRequest):
    try:
        data = crawler_sql_service.save_results(req.task_id, req.results)
        return make_response(1, "Success", data)
    except Exception as e:
        return make_response(0, str(e))

@router_v1.post("/search")
async def search_v1(req: SearchRequest):
    """V1 搜索：需要客户端自己传向量"""
    try:
        vector = req.query_embedding['vector']
        # 注意：这里需要确认你数据库的向量维度。TextEmbedding V3 可能是 1024，V2 是 1536。
        # 请根据你的 PGVector 设置进行匹配。
        if not vector:
            return make_response(2, "Vector is empty", None)

        data = crawler_sql_service.search_knowledge(
            query_embedding=vector,
            task_id=req.task_id,
            limit=req.limit
        )
        return make_response(1, "Search Done", data)
    except Exception as e:
        return make_response(0, str(e))


# ==========================================
# V2 Router: 自动化工作流 (Automated Workflow)
# ==========================================
router_v2 = APIRouter()

@router_v2.post("/auto/map")
async def auto_map(req: AutoMapRequest, background_tasks: BackgroundTasks):
    """
    [异步] 输入首页 URL，自动调用 Firecrawl Map 并入库
    """
    # 也可以放入 background_tasks，但 map 通常比较快，这里演示同步返回任务ID
    try:
        # 为了不阻塞主线程，如果 map 很慢，建议放入 background_tasks
        # 这里为了能立刻看到 task_id，先同步调用 (Firecrawl Map 比较快)
        data = workflow.map_and_ingest(req.url)
        return make_response(1, "Mapping Started", data)
    except Exception as e:
        return make_response(0, str(e))

@router_v2.post("/auto/process")
async def auto_process(req: AutoProcessRequest, background_tasks: BackgroundTasks):
    """
    [异步] 触发后台任务：消费队列 -> 抓取 -> Embedding -> 入库
    """
    # 将耗时操作放入后台任务
    background_tasks.add_task(workflow.process_task_queue, req.task_id, req.batch_size)
    return make_response(1, "Processing started in background", {"task_id": req.task_id})

@router_v2.post("/search")
async def search_v2(req: TextSearchRequest):
    """
    [智能] 输入自然语言文本 -> 后端转向量 -> 搜索
    """
    try:
        data = workflow.search_with_embedding(req.query, req.task_id, req.limit)
        return make_response(1, "Search Success", data)
    except Exception as e:
        return make_response(0, f"Search Failed: {str(e)}")


# ==========================================
# 挂载路由
# ==========================================
app.include_router(router_v1, prefix="/api/v1", tags=["V1 Manual API"])
app.include_router(router_v2, prefix="/api/v2", tags=["V2 Automated Workflow"])

if __name__ == "__main__":
    import uvicorn
    uvicorn.run(app, host="0.0.0.0", port=8000, reload=True)