"""
RAG 检索工具
"""

from langchain_core.tools import tool
from backend.app.logger import info


_rag_pipeline = None


def _get_rag_pipeline():
    """获取或创建 RAG pipeline 单例"""
    global _rag_pipeline
    if _rag_pipeline is None:
        from backend.app.rag.pipeline import RAGPipeline
        _rag_pipeline = RAGPipeline(
            num_queries=3,
            rerank_top_n=5,
            use_rerank=True,
            return_parent_docs=True,
        )
    return _rag_pipeline


def _format_confidence(result) -> str:
    """格式化置信度描述"""
    if result.confidence < 0.4:
        return "低"
    elif result.confidence < 0.6:
        return "中"
    return "高"


@tool
async def rag_search(query: str) -> str:
    """
    检索知识库获取相关信息

    Returns:
        包含检索结果和置信度的结构化回复
    """
    info(f"[Tool] rag_search: {query[:30]}...")
    try:
        pipeline = _get_rag_pipeline()
        result = await pipeline.aretrieve_with_confidence(query, original_query=query)

        if not result.content:
            return "【RAG检索结果】\n未在知识库中找到相关内容。\n置信度：0.0\n建议：可尝试联网搜索获取信息。"

        confidence_desc = _format_confidence(result)
        is_useful_note = "✅ 检索结果可信，可直接使用" if result.is_useful else "⚠️ 检索结果置信度较低，可能需要联网搜索补充"

        response = f"""【RAG检索结果】
{result.content}

【置信度评估】
- 综合置信度：{result.confidence:.2f}（{confidence_desc}）
- 向量相似度：{result.scores['embedding']:.2f}
- 重排分数：{result.scores['rerank']:.2f}
- LLM评估：{result.scores['llm']:.2f}

{is_useful_note}"""

        info(f"[Tool] rag_search 完成: confidence={result.confidence:.3f}, is_useful={result.is_useful}")
        return response

    except Exception as e:
        info(f"[Tool] rag_search 失败: {e}")
        return f"【RAG检索失败】\n错误：{str(e)}\n建议：请稍后重试或使用联网搜索"