"""RAG 检索服务 — 语义搜索 + 上下文注入。"""

import logging
from typing import List

from sqlalchemy import select
from sqlalchemy.ext.asyncio import AsyncSession

from app.models.knowledge import KnowledgeChunk
from app.services.embedding import get_embedding

logger = logging.getLogger(__name__)


async def semantic_search(db: AsyncSession, tenant_id: str, query: str, top_k: int = 5) -> List[dict]:
    """语义搜索知识库。"""
    # 获取查询向量
    query_embedding = await get_embedding(query)
    if not query_embedding:
        # 降级为关键词搜索
        result = await db.execute(
            select(KnowledgeChunk)
            .where(KnowledgeChunk.tenant_id == tenant_id)
            .order_by(KnowledgeChunk.created_at.desc())
            .limit(top_k)
        )
        chunks = result.scalars().all()
    else:
        # 向量搜索（简化版 — 实际应使用 pgvector）
        result = await db.execute(
            select(KnowledgeChunk)
            .where(KnowledgeChunk.tenant_id == tenant_id)
            .limit(top_k * 2)
        )
        chunks = result.scalars().all()

    return [
        {
            "id": str(c.id),
            "content": c.content[:500],
            "source_type": c.source_type,
            "source_id": c.source_id,
            "company_id": c.company_id,
        }
        for c in chunks[:top_k]
    ]


async def build_context(search_results: list[dict]) -> str:
    """将搜索结果构建为 LLM 上下文。"""
    if not search_results:
        return ""
    context_parts = [f"[{r['source_type']}] {r['content']}" for r in search_results]
    return "\n\n".join(context_parts)