import asyncio
from sqlalchemy.ext.asyncio import AsyncSession
from app.database import AsyncSessionLocal, engine
from app.models import Signal, ContextNode, Experiment
from sqlalchemy import text
import uuid

async def seed_data():
    async with AsyncSessionLocal() as session:
        # 1. Clear existing data (optional, but good for a clean start)
        await session.execute(text("TRUNCATE TABLE signals, context_nodes, experiments RESTART IDENTITY CASCADE;"))
        
        # 2. Seed Signals (外部信号 - 触发工作的源头)
        signals = [
            Signal(
                source="HackerNews", 
                content="DeepSeek-V3 release: 671B parameters, beats GPT-4 on coding tasks.", 
                impact_score=0.95,
                processed=False
            ),
            Signal(
                source="Internal User Feedback", 
                content="RAG retrieval is too slow (avg 3s), users are dropping off.", 
                impact_score=0.85,
                processed=False
            ),
            Signal(
                source="Arxiv", 
                content="Paper: 'Lost in the Middle: How Language Models Use Long Contexts'", 
                impact_score=0.6,
                processed=True
            )
        ]
        session.add_all(signals)
        
        # 3. Seed Context Nodes (团队当前的认知/共识)
        context_nodes = [
            ContextNode(
                content="当前 RAG 切片大小设定为 512 tokens，重叠 50 tokens。",
                version=1,
                status="active"
            ),
            ContextNode(
                content="所有 SQL 生成任务默认使用 gpt-4-turbo，因为其指令遵循能力最强。",
                version=2,
                status="active"
            ),
            ContextNode(
                content="向量数据库使用 pgvector，当前索引类型为 ivfflat。",
                version=1,
                status="active"
            )
        ]
        session.add_all(context_nodes)
        
        # 4. Seed Experiments (基于信号和认知制定的行动)
        experiments = [
            Experiment(
                title="评估 DeepSeek-V3 的 SQL 生成能力",
                hypothesis="如果 DeepSeek-V3 在 Spider 数据集上表现达到 95% GPT-4 水平，我们可以替换 GPT-4 以降低 90% 成本。",
                status="running",
                target_metric="accuracy_vs_cost",
                current_score=0.88
            ),
            Experiment(
                title="RAG 延迟优化：混合检索策略",
                hypothesis="引入 BM25 稀疏检索 + 向量检索的混合模式，可以在保持召回率的同时减少重排序候选集，降低延迟。",
                status="draft",
                target_metric="latency_p95",
                current_score=None
            ),
            Experiment(
                title="上下文窗口扩展测试",
                hypothesis="将上下文窗口扩展到 128k，测试在大文档问答中的准确率变化。",
                status="failed",
                target_metric="recall",
                current_score=0.45
            )
        ]
        session.add_all(experiments)
        
        await session.commit()
        print("Database seeded successfully with initial data!")

if __name__ == "__main__":
    asyncio.run(seed_data())