import torch
import itertools
import threading
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from collections import Counter, defaultdict
from loguru import logger
from abc import ABCMeta, abstractmethod
from .paper_client import PaperClient
from .paper_crawling import PaperCrawling
from .llms_api import APIHelper
from .hash import get_embedding_model


class UnionFind:
    def __init__(self, n):
        self.parent = list(range(n))
        self.rank = [1] * n

    def find(self, x):
        if self.parent[x] != x:
            self.parent[x] = self.find(self.parent[x])
        return self.parent[x]

    def union(self, x, y):
        rootX = self.find(x)
        rootY = self.find(y)
        if rootX != rootY:
            if self.rank[rootX] > self.rank[rootY]:
                self.parent[rootY] = rootX
            elif self.rank[rootX] < self.rank[rootY]:
                self.parent[rootX] = rootY
            else:
                self.parent[rootY] = rootX
                self.rank[rootX] += 1


def can_merge(uf, similarity_matrix, i, j, threshold):
    root_i = uf.find(i)
    root_j = uf.find(j)
    for k in range(len(similarity_matrix)):
        if uf.find(k) == root_i or uf.find(k) == root_j:
            if (
                similarity_matrix[i][k] < threshold
                or similarity_matrix[j][k] < threshold
            ):
                return False
    return True


class CoCite:
    _instance = None
    _initialized = False

    def __new__(cls, *args, **kwargs):
        if cls._instance is None:
            cls._instance = super(CoCite, cls).__new__(cls)
        return cls._instance

    def __init__(self) -> None:
        if not self._initialized:
            logger.debug("init co-cite map begin...")
            self.paper_client = PaperClient()
            citemap = self.paper_client.build_citemap()
            self.comap = defaultdict(lambda: defaultdict(int))
            for paper_id, cited_id in citemap.items():
                for id0, id1 in itertools.combinations(cited_id, 2):
                    # ensure comap[id0][id1] == comap[id1][id0]
                    self.comap[id0][id1] += 1
                    self.comap[id1][id0] += 1
            logger.debug("init co-cite map success")
            CoCite._initialized = True

    def get_cocite_ids(self, id_, k=1):
        sorted_items = sorted(self.comap[id_].items(), key=lambda x: x[1], reverse=True)
        top_k = sorted_items[:k]
        paper_ids = []
        for item in top_k:
            paper_ids.append(item[0])
        paper_ids = self.paper_client.filter_paper_id_list(paper_ids)
        return paper_ids


class Retriever(object):
    __metaclass__ = ABCMeta
    retriever_name = "BASE"

    def __init__(self, config):
        self.config = config
        self.use_cocite = config.RETRIEVE.use_cocite
        self.use_cluster_to_filter = config.RETRIEVE.use_cluster_to_filter
        self.paper_client = PaperClient()
        self.cocite = CoCite()
        self.api_helper = APIHelper(config=config)
        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
        self.embedding_model = get_embedding_model(config)
        self.paper_crawling = PaperCrawling(config=config)

    @abstractmethod
    def retrieve(self, bg, entities, use_evaluate):
        pass

    def retrieve_entities_by_enties(self, entities):
        # TODO: KG
        expand_entities = self.paper_client.find_related_entities_by_entity_list(
            entities,
            n=self.config.RETRIEVE.kg_jump_num,
            k=self.config.RETRIEVE.kg_cover_num,
            relation_name=self.config.RETRIEVE.relation_name,
        )
        expand_entities = list(set(entities + expand_entities))
        entity_paper_num_dict = self.paper_client.get_entities_related_paper_num(
            expand_entities
        )
        new_entities = []
        entity_paper_num_dict = {
            k: v for k, v in entity_paper_num_dict.items() if v != 0
        }
        entity_paper_num_dict = dict(
            sorted(entity_paper_num_dict.items(), key=lambda item: item[1])
        )
        sum_paper_num = 0
        for key, value in entity_paper_num_dict.items():
            if sum_paper_num <= self.config.RETRIEVE.sum_paper_num:
                sum_paper_num += value
                new_entities.append(key)
            elif (
                value < self.config.RETRIEVE.limit_num
                and sum_paper_num < self.config.RETRIEVE.sum_paper_num
            ):
                sum_paper_num += value
                new_entities.append(key)
        return new_entities

    def update_related_paper(self, paper_id_list):
        """
        Args:
            paper_id_list: list
        Return:
            related_paper: list(dict)
        """
        related_paper = self.paper_client.update_papers_from_client(paper_id_list)
        return related_paper

    def calculate_similarity(self, entities, related_entities_list, use_weight=False):
        if use_weight:
            vec1 = self.vectorizer.transform([" ".join(entities)]).toarray()[0]
            weighted_vec1 = np.array(
                [
                    vec1[i] * self.log_inverse_freq.get(word, 1)
                    for i, word in enumerate(self.vectorizer.get_feature_names_out())
                ]
            )
            vecs2 = self.vectorizer.transform(
                [
                    " ".join(related_entities)
                    for related_entities in related_entities_list
                ]
            ).toarray()
            weighted_vecs2 = np.array(
                [
                    [
                        vec2[i] * self.log_inverse_freq.get(word, 1)
                        for i, word in enumerate(
                            self.vectorizer.get_feature_names_out()
                        )
                    ]
                    for vec2 in vecs2
                ]
            )
            similarity = cosine_similarity([weighted_vec1], weighted_vecs2)[0]
        else:
            vec1 = self.vectorizer.transform([" ".join(entities)])
            vecs2 = self.vectorizer.transform(
                [
                    " ".join(related_entities)
                    for related_entities in related_entities_list
                ]
            )
            similarity = cosine_similarity(vec1, vecs2)[0]
        return similarity

    def cal_related_score(
        self, embedding, related_paper_id_list, type_name="embedding"
    ):
        score_1 = np.zeros((len(related_paper_id_list)))
        # score_2 = np.zeros((len(related_paper_id_list)))
        origin_vector = torch.tensor(embedding).to(self.device).unsqueeze(0)
        context_embeddings = self.paper_client.get_papers_attribute(
            related_paper_id_list, type_name
        )
        if len(context_embeddings) > 0:
            context_embeddings = torch.tensor(context_embeddings).to(self.device)
            score_1 = torch.nn.functional.cosine_similarity(
                origin_vector, context_embeddings
            )
            score_1 = score_1.cpu().numpy()
            if self.config.RETRIEVE.need_normalize:
                score_1 = score_1 / np.max(score_1)
        score_all_dict = dict(zip(related_paper_id_list, score_1))
        # score_en_dict = dict(zip(related_paper_id_list, score_2))
        """
        score_all_dict = dict(
            zip(
                related_paper_id_list,
                score_1 * self.config.RETRIEVE.alpha
                + score_2 * self.config.RETRIEVE.beta,
            )
        )
        """
        return {}, {}, score_all_dict

    def filter_related_paper(self, score_dict, top_k):
        if len(score_dict) <= top_k:
            return list(score_dict.keys())
        if not self.use_cluster_to_filter:
            paper_id_list = (
                list(score_dict.keys())[:top_k]
                if len(score_dict) >= top_k
                else list(score_dict.keys())
            )
            return paper_id_list
        else:
            # clustering filter, ensure that each category the highest score save first
            paper_id_list = list(score_dict.keys())
            paper_embedding_list = [
                self.paper_client.get_paper_attribute(paper_id, "embedding")
                for paper_id in paper_id_list
            ]
            paper_embedding = np.array(paper_embedding_list)
            paper_embedding_list = [
                self.paper_client.get_paper_attribute(
                    paper_id, "contribution_embedding"
                )
                for paper_id in paper_id_list
            ]
            paper_contribution_embedding = np.array(paper_embedding_list)
            paper_embedding_list = [
                self.paper_client.get_paper_attribute(paper_id, "summary_embedding")
                for paper_id in paper_id_list
            ]
            paper_summary_embedding = np.array(paper_embedding_list)
            weight_embedding = self.config.RETRIEVE.s_bg
            weight_contribution = self.config.RETRIEVE.s_contribution
            weight_summary = self.config.RETRIEVE.s_summary
            paper_embedding = (
                weight_embedding * paper_embedding
                + weight_contribution * paper_contribution_embedding
                + weight_summary * paper_summary_embedding
            )
            similarity_matrix = np.dot(paper_embedding, paper_embedding.T)
            related_labels = self.cluster_algorithm(paper_id_list, similarity_matrix)
            related_paper_label_dict = dict(zip(paper_id_list, related_labels))
            label_group = {}
            for paper_id, label in related_paper_label_dict.items():
                if label not in label_group:
                    label_group[label] = []
                label_group[label].append(paper_id)
            paper_id_list = []
            while len(paper_id_list) < top_k:
                for label, papers in label_group.items():
                    if papers:
                        paper_id_list.append(papers.pop(0))
                        if len(paper_id_list) >= top_k:
                            break
            return paper_id_list

    def cosine_similarity_search(self, embedding, k=1, type_name="embedding"):
        """
        return related paper: list
        """
        result = self.paper_client.cosine_similarity_search(
            embedding, k, type_name=type_name
        )
        # backtrack: first is itself
        result = result[1:]
        return result

    def cluster_algorithm(self, paper_id_list, similarity_matrix):
        threshold = self.config.RETRIEVE.similarity_threshold
        uf = UnionFind(len(paper_id_list))
        # merge
        for i in range(len(similarity_matrix)):
            for j in range(i + 1, len(similarity_matrix)):
                if similarity_matrix[i][j] >= threshold:
                    if can_merge(uf, similarity_matrix, i, j, threshold):
                        uf.union(i, j)
        cluster_labels = [uf.find(i) for i in range(len(similarity_matrix))]
        return cluster_labels

    def eval_related_paper_in_all(self, score_all_dict, target_paper_id_list):
        score_all_dict = dict(
            sorted(score_all_dict.items(), key=lambda item: item[1], reverse=True)
        )
        result = {}
        related_paper_id_list = list(score_all_dict.keys())
        if len(related_paper_id_list) == 0:
            for k in self.config.RETRIEVE.top_k_list:
                result[k] = {"recall": 0, "precision": 0}
            return result, 0, 0, 0
        all_paper_id_set = set(related_paper_id_list)
        all_paper_id_set.update(target_paper_id_list)
        all_paper_id_list = list(all_paper_id_set)
        paper_embedding_list = [
            self.paper_client.get_paper_attribute(paper_id, "embedding")
            for paper_id in target_paper_id_list
        ]
        paper_embedding = np.array(paper_embedding_list)
        paper_embedding_list = [
            self.paper_client.get_paper_attribute(paper_id, "contribution_embedding")
            for paper_id in target_paper_id_list
        ]
        paper_contribution_embedding = np.array(paper_embedding_list)
        paper_embedding_list = [
            self.paper_client.get_paper_attribute(paper_id, "summary_embedding")
            for paper_id in target_paper_id_list
        ]
        paper_summary_embedding = np.array(paper_embedding_list)
        weight_embedding = self.config.RETRIEVE.s_bg
        weight_contribution = self.config.RETRIEVE.s_contribution
        weight_summary = self.config.RETRIEVE.s_summary
        target_paper_embedding = (
            weight_embedding * paper_embedding
            + weight_contribution * paper_contribution_embedding
            + weight_summary * paper_summary_embedding
        )
        similarity_threshold = self.config.RETRIEVE.similarity_threshold
        similarity_matrix = np.dot(target_paper_embedding, target_paper_embedding.T)
        target_labels = self.cluster_algorithm(target_paper_id_list, similarity_matrix)
        target_paper_label_dict = dict(zip(target_paper_id_list, target_labels))
        logger.debug("Target paper cluster result: {}".format(target_paper_label_dict))
        logger.debug(
            {
                paper_id: self.paper_client.get_paper_attribute(paper_id, "title")
                for paper_id in target_paper_label_dict.keys()
            }
        )

        all_labels = []
        for paper_id in all_paper_id_list:
            paper_bg_embedding = [
                self.paper_client.get_paper_attribute(paper_id, "embedding")
            ]
            paper_bg_embedding = np.array(paper_bg_embedding)
            paper_contribution_embedding = [
                self.paper_client.get_paper_attribute(
                    paper_id, "contribution_embedding"
                )
            ]
            paper_contribution_embedding = np.array(paper_contribution_embedding)
            paper_summary_embedding = [
                self.paper_client.get_paper_attribute(paper_id, "summary_embedding")
            ]
            paper_summary_embedding = np.array(paper_summary_embedding)
            paper_embedding = (
                weight_embedding * paper_bg_embedding
                + weight_contribution * paper_contribution_embedding
                + weight_summary * paper_summary_embedding
            )
            similarities = cosine_similarity(paper_embedding, target_paper_embedding)[0]
            if np.any(similarities >= similarity_threshold):
                all_labels.append(target_labels[np.argmax(similarities)])
            else:
                all_labels.append(-1)  # other class: -1
        all_paper_label_dict = dict(zip(all_paper_id_list, all_labels))
        all_label_counts = Counter(all_paper_label_dict.values())
        logger.debug(f"all label counts : {all_label_counts}")
        target_label_counts = Counter(target_paper_label_dict.values())
        logger.debug(f"target label counts : {target_label_counts}")
        target_label_list = list(target_label_counts.keys())
        max_k = max(self.config.RETRIEVE.top_k_list)
        logger.info("=== Begin filter related paper ===")
        max_k_paper_id_list = self.filter_related_paper(score_all_dict, top_k=max_k)
        logger.info("=== End filter related paper ===")
        for k in self.config.RETRIEVE.top_k_list:
            # 前top k 的文章
            top_k = min(k, len(max_k_paper_id_list))
            top_k_paper_id_list = max_k_paper_id_list[:top_k]
            top_k_paper_label_dict = {}
            for paper_id in top_k_paper_id_list:
                top_k_paper_label_dict[paper_id] = all_paper_label_dict[paper_id]
            logger.debug(
                "=== top k {} paper id list : {}".format(k, top_k_paper_label_dict)
            )
            logger.debug(
                {
                    paper_id: self.paper_client.get_paper_attribute(paper_id, "title")
                    for paper_id in top_k_paper_label_dict.keys()
                }
            )
            top_k_label_counts = Counter(top_k_paper_label_dict.values())
            logger.debug(f"top K label counts : {top_k_label_counts}")
            top_k_label_list = list(top_k_label_counts.keys())
            match_label_list = list(set(target_label_list) & set(top_k_label_list))
            logger.debug(f"match label list : {match_label_list}")
            recall = 0
            precision = 0
            for label in match_label_list:
                recall += target_label_counts[label]
            for label in match_label_list:
                precision += top_k_label_counts[label]
            recall /= len(target_paper_id_list)
            precision /= len(top_k_paper_id_list)
            result[k] = {"recall": recall, "precision": precision}

        related_paper_id_list = list(score_all_dict.keys())
        related_paper_label_dict = {}
        for paper_id in related_paper_id_list:
            related_paper_label_dict[paper_id] = all_paper_label_dict[paper_id]
        related_label_counts = Counter(related_paper_label_dict.values())
        logger.debug(f"top K label counts : {related_label_counts}")
        related_label_list = list(related_label_counts.keys())
        match_label_list = list(set(target_label_list) & set(related_label_list))
        recall = 0
        precision = 0
        for label in match_label_list:
            recall += target_label_counts[label]
        for label in match_label_list:
            precision += related_label_counts[label]
        recall /= len(target_paper_id_list)
        precision /= len(related_paper_id_list)
        logger.debug(result)
        return result, len(target_label_counts), recall, precision


class RetrieverFactory(object):
    _instance = None
    _lock = threading.Lock()

    def __new__(cls, *args, **kwargs):
        with cls._lock:
            if cls._instance is None:
                cls._instance = super(RetrieverFactory, cls).__new__(
                    cls, *args, **kwargs
                )
                cls._instance.init_factory()
        return cls._instance

    def init_factory(self):
        self.retriever_classes = {}

    @staticmethod
    def get_retriever_factory():
        if RetrieverFactory._instance is None:
            RetrieverFactory._instance = RetrieverFactory()
        return RetrieverFactory._instance

    def register_retriever(self, retriever_name, retriever_class) -> bool:
        if retriever_name not in self.retriever_classes:
            self.retriever_classes[retriever_name] = retriever_class
            return True
        else:
            return False

    def delete_retriever(self, retriever_name) -> bool:
        if retriever_name in self.retriever_classes:
            self.retriever_classes[retriever_name] = None
            del self.retriever_classes[retriever_name]
            return True
        else:
            return False

    def __getitem__(self, key):
        return self.retriever_classes[key]

    def __len__(self):
        return len(self.retriever_classes)

    def create_retriever(self, retriever_name, *args, **kwargs) -> Retriever:
        if retriever_name not in self.retriever_classes:
            raise ValueError(f"Unknown retriever type: {retriever_name}")
        else:
            return self.retriever_classes[retriever_name](*args, **kwargs)


class autoregister:
    def __init__(self, retriever_name, *args, **kwds):
        self.retriever_name = retriever_name

    def __call__(self, cls, *args, **kwds):
        if RetrieverFactory.get_retriever_factory().register_retriever(
            self.retriever_name, cls
        ):
            cls.retriever_name = self.retriever_name
            return cls
        else:
            raise KeyError()


@autoregister("SN")
class SNRetriever(Retriever):
    def __init__(self, config):
        super().__init__(config)

    def retrieve_paper(self, bg):
        entities = []
        embedding = self.embedding_model.encode(bg, device=self.device)
        sn_paper_id_list = self.cosine_similarity_search(
            embedding=embedding,
            k=self.config.RETRIEVE.sn_retrieve_paper_num,
        )
        related_paper = set()
        related_paper.update(sn_paper_id_list)
        cocite_id_set = set()
        if self.use_cocite:
            for paper_id in related_paper:
                cocite_id_set.update(
                    self.cocite.get_cocite_ids(
                        paper_id, k=self.config.RETRIEVE.cocite_top_k
                    )
                )
            related_paper = related_paper.union(cocite_id_set)
        related_paper = list(related_paper)
        logger.debug(f"paper num before filter: {len(related_paper)}")
        result = {
            "embedding": embedding,
            "paper": related_paper,
            "entities": entities,
            "cocite_paper": list(cocite_id_set),
        }
        return result

    def retrieve(self, bg, entities, need_evaluate=True, target_paper_id_list=[]):
        """
        Args:
            context: string
        Return:
            list(dict)
        """
        if need_evaluate:
            if target_paper_id_list is None or len(target_paper_id_list) == 0:
                logger.error(
                    "If you need evaluate retriever, please input target paper is list..."
                )
            else:
                target_paper_id_list = list(set(target_paper_id_list))
        retrieve_result = self.retrieve_paper(bg)
        related_paper_id_list = retrieve_result["paper"]
        retrieve_paper_num = len(related_paper_id_list)
        _, _, score_all_dict = self.cal_related_score(
            retrieve_result["embedding"], related_paper_id_list=related_paper_id_list
        )
        top_k_matrix = {}
        recall = 0
        precision = 0
        filtered_recall = 0
        filtered_precision = 0
        if need_evaluate:
            top_k_matrix, label_num, recall, precision = self.eval_related_paper_in_all(
                score_all_dict, target_paper_id_list
            )
            logger.debug("Top P matrix:{}".format(top_k_matrix))
            logger.debug("before filter:")
            logger.debug(f"Recall: {recall:.3f}")
            logger.debug(f"Precision: {precision:.3f}")
        related_paper = self.filter_related_paper(score_all_dict, top_k=10)
        related_paper = self.update_related_paper(related_paper)
        result = {
            "recall": recall,
            "precision": precision,
            "filtered_recall": filtered_recall,
            "filtered_precision": filtered_precision,
            "related_paper": related_paper,
            "related_paper_id_list": related_paper_id_list,
            "cocite_paper_id_list": retrieve_result["cocite_paper"],
            "entities": retrieve_result["entities"],
            "top_k_matrix": top_k_matrix,
            "gt_reference_num": len(target_paper_id_list),
            "retrieve_paper_num": retrieve_paper_num,
            "label_num": label_num,
        }
        return result


@autoregister("KG")
class KGRetriever(Retriever):
    def __init__(self, config):
        super().__init__(config)

    def retrieve_paper(self, entities):
        new_entities = self.retrieve_entities_by_enties(entities)
        logger.debug("KG entities for retriever: {}".format(new_entities))
        related_paper = set()
        for entity in new_entities:
            paper_id_set = set(self.paper_client.find_paper_by_entity(entity))
            related_paper = related_paper.union(paper_id_set)
        cocite_id_set = set()
        if self.use_cocite:
            for paper_id in related_paper:
                cocite_id_set.update(self.cocite.get_cocite_ids(paper_id))
            related_paper = related_paper.union(cocite_id_set)
        related_paper = list(related_paper)
        logger.debug(f"paper num before filter: {len(related_paper)}")
        result = {
            "paper": related_paper,
            "entities": entities,
            "cocite_paper": list(cocite_id_set),
        }
        return result

    def retrieve(self, bg, entities, need_evaluate=False, target_paper_id_list=[]):
        """
        Args:
            context: string
        Return:
            list(dict)
        """
        if need_evaluate:
            if target_paper_id_list is None or len(target_paper_id_list) == 0:
                logger.error(
                    "If you need evaluate retriever, please input target paper is list..."
                )
            else:
                target_paper_id_list = list(set(target_paper_id_list))
                logger.debug(f"target paper id list: {target_paper_id_list}")
        retrieve_result = self.retrieve_paper(entities)
        related_paper_id_list = retrieve_result["paper"]
        retrieve_paper_num = len(related_paper_id_list)
        embedding = self.embedding_model.encode(bg, device=self.device)
        _, _, score_all_dict = self.cal_related_score(
            embedding, related_paper_id_list=related_paper_id_list
        )
        top_k_matrix = {}
        recall = 0
        precision = 0
        filtered_recall = 0
        filtered_precision = 0
        if need_evaluate:
            top_k_matrix, label_num, recall, precision = self.eval_related_paper_in_all(
                score_all_dict, target_paper_id_list
            )
            logger.debug("Top P ACC:{}".format(top_k_matrix))
            logger.debug("before filter:")
            logger.debug(f"Recall: {recall:.3f}")
            logger.debug(f"Precision: {precision:.3f}")
        related_paper = self.filter_related_paper(score_all_dict, top_k=10)
        related_paper = self.update_related_paper(related_paper)
        result = {
            "recall": recall,
            "precision": precision,
            "filtered_recall": filtered_recall,
            "filtered_precision": filtered_precision,
            "related_paper": related_paper,
            "related_paper_id_list": related_paper_id_list,
            "cocite_paper_id_list": retrieve_result["cocite_paper"],
            "entities": retrieve_result["entities"],
            "top_k_matrix": top_k_matrix,
            "gt_reference_num": len(target_paper_id_list),
            "retrieve_paper_num": retrieve_paper_num,
            "label_num": label_num,
        }
        return result


@autoregister("SNKG")
class SNKGRetriever(Retriever):
    def __init__(self, config):
        super().__init__(config)

    def retrieve_paper(self, bg, entities):
        sn_entities = []
        embedding = self.embedding_model.encode(bg, device=self.device)
        sn_paper_id_list = self.cosine_similarity_search(
            embedding, k=self.config.RETRIEVE.sn_num_for_entity
        )
        related_paper = set()
        related_paper.update(sn_paper_id_list)
        sn_entities += self.paper_client.find_entities_by_paper_list(sn_paper_id_list)
        logger.debug("SN entities for retriever: {}".format(sn_entities))
        entities = list(set(entities + sn_entities))
        new_entities = self.retrieve_entities_by_enties(entities)
        logger.debug("SNKG entities for retriever: {}".format(new_entities))
        for entity in new_entities:
            paper_id_set = set(self.paper_client.find_paper_by_entity(entity))
            related_paper = related_paper.union(paper_id_set)
        cocite_id_set = set()
        if self.use_cocite:
            for paper_id in related_paper:
                cocite_id_set.update(self.cocite.get_cocite_ids(paper_id))
            related_paper = related_paper.union(cocite_id_set)
        related_paper = list(related_paper)
        result = {
            "embedding": embedding,
            "paper": related_paper,
            "entities": entities,
            "cocite_paper": list(cocite_id_set),
        }
        return result

    def retrieve(
        self, bg, entities, need_evaluate=True, target_paper_id_list=[], top_k=10
    ):
        """
        Args:
            context: string
        Return:
            list(dict)
        """
        if need_evaluate:
            if target_paper_id_list is None or len(target_paper_id_list) == 0:
                logger.error(
                    "If you need evaluate retriever, please input target paper is list..."
                )
            else:
                target_paper_id_list = list(set(target_paper_id_list))
                logger.debug(f"target paper id list: {target_paper_id_list}")
        retrieve_result = self.retrieve_paper(bg, entities)
        related_paper_id_list = retrieve_result["paper"]
        retrieve_paper_num = len(related_paper_id_list)
        logger.info("=== Begin cal related paper score ===")
        _, _, score_all_dict = self.cal_related_score(
            retrieve_result["embedding"], related_paper_id_list=related_paper_id_list
        )
        logger.info("=== End cal related paper score ===")
        top_k_matrix = {}
        recall = 0
        precision = 0
        filtered_recall = 0
        filtered_precision = 0
        label_num = 0
        if need_evaluate:
            top_k_matrix, label_num, recall, precision = self.eval_related_paper_in_all(
                score_all_dict, target_paper_id_list
            )
            logger.debug("Top K matrix:{}".format(top_k_matrix))
            logger.debug("before filter:")
            logger.debug(f"Recall: {recall:.3f}")
            logger.debug(f"Precision: {precision:.3f}")
        logger.info("=== Begin filter related paper score ===")
        related_paper = self.filter_related_paper(score_all_dict, top_k)
        logger.info("=== End filter related paper score ===")
        related_paper = self.update_related_paper(related_paper)
        result = {
            "recall": recall,
            "precision": precision,
            "filtered_recall": filtered_recall,
            "filtered_precision": filtered_precision,
            "related_paper": related_paper,
            "cocite_paper_id_list": retrieve_result["cocite_paper"],
            "related_paper_id_list": related_paper_id_list,
            "entities": retrieve_result["entities"],
            "top_k_matrix": top_k_matrix,
            "gt_reference_num": (
                len(target_paper_id_list) if target_paper_id_list is not None else 0
            ),
            "retrieve_paper_num": retrieve_paper_num,
            "label_num": label_num,
        }
        return result