from typing import List, Dict, Optional, Any

from .base_search import BaseSearch

from applications.utils.neo4j import AsyncGraphExpansion
from applications.utils.elastic_search import ElasticSearchStrategy


class HybridSearch(BaseSearch):
    def __init__(self, milvus_pool, es_pool, graph_pool):
        super().__init__(milvus_pool, es_pool)
        self.es_strategy = ElasticSearchStrategy(self.es_pool)
        self.graph_expansion = AsyncGraphExpansion(driver=graph_pool)

    async def hybrid_search(
        self,
        filters: Dict[str, Any],  # 条件过滤
        query_vec: List[float],  # query 的向量
        anns_field: str = "vector_text",  # query指定的向量空间
        search_params: Optional[Dict[str, Any]] = None,  # 向量距离方式
        query_text: str = None,  # 是否通过 topic 倒排
        _source=False,  # 是否返回元数据
        es_size: int = 10000,  # es 第一层过滤数量
        sort_by: str = None,  # 排序
        milvus_size: int = 10,  # milvus粗排返回数量
    ):
        milvus_ids = await self.es_strategy.base_search(
            filters=filters,
            text_query=query_text,
            _source=_source,
            size=es_size,
            sort_by=sort_by,
        )
        if not milvus_ids:
            return {"results": []}
        milvus_ids_list = ",".join(milvus_ids)
        expr = f"id in [{milvus_ids_list}]"
        return await self.base_vector_search(
            query_vec=query_vec,
            anns_field=anns_field,
            limit=milvus_size,
            expr=expr,
            search_params=search_params,
        )

    async def hybrid_search_with_graph(
        self,
        filters: Dict[str, Any],  # 条件过滤
        query_vec: List[float],  # query 的向量
        anns_field: str = "vector_text",  # query指定的向量空间
        search_params: Optional[Dict[str, Any]] = None,  # 向量距离方式
        query_text: str = None,  # 是否通过 topic 倒排
        _source=False,  # 是否返回元数据
        es_size: int = 10000,  # es 第一层过滤数量
        sort_by: str = None,  # 排序
        milvus_size: int = 10,  # milvus粗排返回数量
        co_occurrence_fields: Dict[str, Any] = None,  # 共现字段
        shortest_path_fields: Dict[str, Any] = None,  # 最短之间的 chunks
    ):
        # step1, use elastic_search to filter chunks
        es_milvus_ids = await self.es_strategy.base_search(
            filters=filters,
            text_query=query_text,
            _source=_source,
            size=es_size,
            sort_by=sort_by,
        )
        # step2, use graph to get co_occurrence chunks
        if not co_occurrence_fields:
            co_occurrence_ids = []
        else:
            # 测试版本先只用实体
            seed_label = "Entity"
            name = co_occurrence_fields.get(seed_label)
            if not name:
                co_occurrence_ids = []
            else:
                co_occurrence_ids = await self.graph_expansion.co_occurrence(
                    seed_name=name, seed_label=seed_label
                )

        # step3, 查询两个 chunk 之间的chunks
        if not shortest_path_fields:
            shortest_path_ids = []
        else:
            shortest_path_ids = await self.graph_expansion.shortest_path_chunks(
                a_name=shortest_path_fields.get("a_name"),
                a_label=shortest_path_fields.get("a_label"),
                b_name=shortest_path_fields.get("b_name"),
                b_label=shortest_path_fields.get("b_label"),
            )
        print("es:", es_milvus_ids)
        print("co:", co_occurrence_ids)
        print("shortest:", shortest_path_ids)

        # step3， merge 上述 ids
        final_milvus_ids = list(
            set(shortest_path_ids + co_occurrence_ids + es_milvus_ids)
        )

        # step4, 通过向量获取候选集
        if not final_milvus_ids:
            return {"results": []}

        milvus_ids_list = ",".join(final_milvus_ids)

        expr = f"id in [{milvus_ids_list}]"
        return await self.base_vector_search(
            query_vec=query_vec,
            anns_field=anns_field,
            limit=milvus_size,
            expr=expr,
            search_params=search_params,
        )

    async def expand_with_graph(
        self, milvus_ids: List[int], limit: int
    ) -> List[Dict[str, Any]]:
        """拓展字段"""
        expanded_chunks = await self.graph_expansion.expand_candidates(
            seed_ids=milvus_ids, limit=limit
        )
        return expanded_chunks