"""GraphRAG 检索编排器。

并行执行向量检索和图谱检索，融合排序后构建统一上下文。
"""

from __future__ import annotations

import asyncio

from app.core.logging import get_logger
from app.module.kg_graphrag.context_builder import build_context, textualize_subgraph
from app.module.kg_graphrag.kg_client import KGServiceClient
from app.module.kg_graphrag.milvus_client import MilvusVectorRetriever
from app.module.kg_graphrag.models import (
    EntitySummary,
    GraphContext,
    RelationSummary,
    RetrievalContext,
    RetrievalStrategy,
    VectorChunk,
)

logger = get_logger(__name__)


class GraphRAGRetriever:
    """GraphRAG 检索编排器。"""

    def __init__(
        self,
        *,
        milvus_client: MilvusVectorRetriever,
        kg_client: KGServiceClient,
    ) -> None:
        self._milvus = milvus_client
        self._kg = kg_client

    @classmethod
    def from_settings(cls) -> GraphRAGRetriever:
        return cls(
            milvus_client=MilvusVectorRetriever.from_settings(),
            kg_client=KGServiceClient.from_settings(),
        )

    async def retrieve(
        self,
        query: str,
        collection_name: str,
        graph_id: str,
        strategy: RetrievalStrategy,
        user_id: str = "",
    ) -> RetrievalContext:
        """并行执行向量检索 + 图谱检索，融合结果。"""
        # 构建并行任务
        tasks: dict[str, asyncio.Task] = {}

        if strategy.enable_vector:
            # 先校验 collection 存在性，防止越权访问
            if not await self._milvus.has_collection(collection_name):
                logger.warning(
                    "Collection %s not found, skipping vector retrieval",
                    collection_name,
                )
            else:
                tasks["vector"] = asyncio.create_task(
                    self._milvus.search(
                        collection_name=collection_name,
                        query=query,
                        top_k=strategy.vector_top_k,
                    )
                )

        if strategy.enable_graph:
            tasks["graph"] = asyncio.create_task(
                self._retrieve_graph(
                    query=query,
                    graph_id=graph_id,
                    strategy=strategy,
                    user_id=user_id,
                )
            )

        # 等待所有任务完成
        if tasks:
            await asyncio.gather(*tasks.values(), return_exceptions=True)

        # 收集结果
        vector_chunks: list[VectorChunk] = []
        if "vector" in tasks:
            try:
                vector_chunks = tasks["vector"].result()
            except Exception:
                logger.exception("Vector retrieval task failed")

        entities: list[EntitySummary] = []
        relations: list[RelationSummary] = []
        if "graph" in tasks:
            try:
                entities, relations = tasks["graph"].result()
            except Exception:
                logger.exception("Graph retrieval task failed")

        # 融合排序
        vector_chunks = self._rank_results(
            vector_chunks, entities, relations, strategy
        )

        # 三元组文本化
        graph_text = textualize_subgraph(entities, relations)

        # 构建上下文
        merged_text = build_context(
            vector_chunks,
            graph_text,
            vector_weight=strategy.vector_weight,
            graph_weight=strategy.graph_weight,
        )

        return RetrievalContext(
            vector_chunks=vector_chunks,
            graph_context=GraphContext(
                entities=entities,
                relations=relations,
                textualized=graph_text,
            ),
            merged_text=merged_text,
        )

    async def _retrieve_graph(
        self,
        query: str,
        graph_id: str,
        strategy: RetrievalStrategy,
        user_id: str,
    ) -> tuple[list[EntitySummary], list[RelationSummary]]:
        """图谱检索：全文搜索 -> 种子实体 -> 子图扩展。"""
        # 1. 全文检索获取种子实体
        seed_entities = await self._kg.fulltext_search(
            graph_id=graph_id,
            query=query,
            size=strategy.graph_max_entities,
            user_id=user_id,
        )

        if not seed_entities:
            logger.debug("No seed entities found for query: %s", query)
            return [], []

        # 2. 获取种子实体的 N-hop 子图
        seed_ids = [e.id for e in seed_entities]
        entities, relations = await self._kg.get_subgraph(
            graph_id=graph_id,
            entity_ids=seed_ids,
            depth=strategy.graph_depth,
            user_id=user_id,
        )

        logger.info(
            "Graph retrieval: %d seed entities -> %d entities, %d relations",
            len(seed_entities), len(entities), len(relations),
        )
        return entities, relations

    def _rank_results(
        self,
        vector_chunks: list[VectorChunk],
        entities: list[EntitySummary],
        relations: list[RelationSummary],
        strategy: RetrievalStrategy,
    ) -> list[VectorChunk]:
        """对向量检索结果进行融合排序。

        基于向量分数归一化后加权排序。图谱关联度通过实体度数近似评估。
        """
        if not vector_chunks:
            return vector_chunks

        # 向量分数归一化 (min-max scaling)
        scores = [c.score for c in vector_chunks]
        min_score = min(scores)
        max_score = max(scores)
        score_range = max_score - min_score

        # 构建图谱实体名称集合，用于关联度加分
        graph_entity_names = {e.name.lower() for e in entities}

        ranked: list[tuple[float, VectorChunk]] = []
        for chunk in vector_chunks:
            # 归一化向量分数
            norm_score = (
                (chunk.score - min_score) / score_range
                if score_range > 0
                else 1.0
            )

            # 图谱关联度加分：文档片段中提及图谱实体名称
            graph_boost = 0.0
            if graph_entity_names:
                chunk_text_lower = chunk.text.lower()
                mentioned = sum(
                    1 for name in graph_entity_names if name in chunk_text_lower
                )
                graph_boost = min(mentioned / max(len(graph_entity_names), 1), 1.0)

            # 加权融合分数
            final_score = (
                strategy.vector_weight * norm_score
                + strategy.graph_weight * graph_boost
            )
            ranked.append((final_score, chunk))

        # 按融合分数降序排序
        ranked.sort(key=lambda x: x[0], reverse=True)
        return [chunk for _, chunk in ranked]