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SciPIP / src /utils /paper_client.py
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r"""_summary_
-*- coding: utf-8 -*-
Module : prompt.utils
File Name : utils.paper_client
Description : paper client, all operations about neo4j database are in PaperClient
Creation Date : 2024-11-09
Modification Date : 2024-12-17
Author : Lihui Gu (code), Wenxiao Wang (comments)
"""
import os
import re
import json
import torch
from tqdm import tqdm
from neo4j import GraphDatabase
from collections import defaultdict, deque
from py2neo import Graph, Node, Relationship
from loguru import logger


class PaperClient:
    _instance = None
    _initialized = False

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

    def __init__(self) -> None:
        if not self._initialized:
            self.driver = self.get_neo4j_driver()
            self.teb_model = None
            self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
            PaperClient._initialized = True

    def get_neo4j_driver(self):
        URI = os.environ["NEO4J_URL"]
        NEO4J_USERNAME = os.environ["NEO4J_USERNAME"]
        NEO4J_PASSWD = os.environ["NEO4J_PASSWD"]
        AUTH = (NEO4J_USERNAME, NEO4J_PASSWD)
        driver = GraphDatabase.driver(URI, auth=AUTH)
        return driver

    def update_paper_from_client(self, paper):
        """Read paper from the database (client), update it info into `paper`
        Args:
            paper (str): a paper's hash_id
        Returns:
            None
        """
        paper_id = paper.get("hash_id", None)
        if paper_id is None:
            return None
        query = f"""
            MATCH (p:Paper {{hash_id: {paper_id}}})
            RETURN p
            """
        with self.driver.session() as session:
            result = session.execute_read(lambda tx: tx.run(query).data())
        if result:
            paper_from_client = result[0]["p"]
            if paper_from_client is not None:
                paper.update(paper_from_client)

    def update_papers_from_client(self, paper_id_list):
        """Read paper from the database (client)
        Args:
            paper_id_list (List of str)
        Returns:
            List of papers read from the database
        """
        query = """
            UNWIND $papers AS paper
            MATCH (p:Paper {hash_id: paper.hash_id})
            RETURN p as result
        """
        paper_data = [
            {
                "hash_id": hash_id,
            }
            for hash_id in paper_id_list
        ]
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, papers=paper_data).data()
            )
        return [r["result"] for r in result]

    def get_paper_attribute(self, paper_id, attribute_name):
        """Get some attribute of a certain paper
        Args:
            paper_id (str):
            attribute_name (str):
        Returns:
            The certain attribute
        """
        query = f"""
            MATCH (p:Paper {{hash_id: {paper_id}}})
            RETURN p.{attribute_name} AS attributeValue
            """
        with self.driver.session() as session:
            result = session.execute_read(lambda tx: tx.run(query).data())
        if result:
            return result[0]["attributeValue"]
        else:
            logger.error(f"paper id {paper_id} get {attribute_name} failed.")
            return None

    def get_papers_attribute(self, paper_id_list, attribute_name):
        """Get some attribute of a list of papers
        Args:
            paper_id (List of str):
            attribute_name (str):
        Returns:
            List of certain attribute
        """
        query = """
            UNWIND $paper_ids AS paper_id
            MATCH (p:Paper {hash_id: paper_id})
            RETURN p.hash_id AS hash_id, p[$attribute_name] AS attributeValue
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(
                    query, paper_ids=paper_id_list, attribute_name=attribute_name
                ).data()
            )
        paper_attributes = [record["attributeValue"] for record in result]
        return paper_attributes

    def get_paper_by_attribute(self, attribute_name, anttribute_value):
        """Get some paper whose `attribute_name` is exactly equal to `anttribute_value`
        Args:
            anttribute_name
            anttribute_value
        Returns:
            The first exact match paper object or None
        """
        query = f"""
            MATCH (p:Paper {{{attribute_name}: '{anttribute_value}'}})
            RETURN p
            """
        with self.driver.session() as session:
            result = session.execute_read(lambda tx: tx.run(query).data())
        if result:
            return result[0]["p"]
        else:
            return None

    def get_paper_from_term(self, entity):
        """Get paper from entity. The method is so strict that paper.entities must be 
        exactly equal to entity. The method is not used now.
        Args:
            entity:
        Returns:
        """
        if entity is None:
            return None
        query = """
            MATCH (p:Paper)
            WHERE p.entity = $entity
            RETURN p.hash_id as hash_id
            """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, entity=entity).data()
            )
        if result:
            return [record["hash_id"] for record in result]
        else:
            return []

    def find_related_entities_by_entity_list(
        self, entity_names, n=1, k=3, relation_name="related"
    ):
        """Find all entities related to an entity name
        Args:
            entity_names (List): list of entities
            n: not used
            k: entity a and b are related if they co-occure in at least `k` papers
        Returns:
            related_entities (List): list of entities who are related with any entity in `entity_names`
        """
        related_entities = set()
        query = """
            UNWIND $batch_entities AS entity_name
            MATCH (e1:Entity {name: entity_name})-[:RELATED_TO]->(p:Paper)<-[:RELATED_TO]-(e2:Entity)
            WHERE e1 <> e2
            WITH e1, e2, COUNT(p) AS common_papers, entity_name
            WHERE common_papers > $k
            RETURN e2.name AS entities, entity_name AS source_entity, common_papers
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, batch_entities=entity_names, k=k).data()
            )
        for record in result:
            entity = record["entities"]
            related_entities.add(entity)
        return list(related_entities)

    def find_entities_by_paper_list(self, hash_ids: list):
        """Retrieve entities for a list of papers:
        Args:
            hash_ids (List of papers):
        Returns:
            entity_list (List of List of entities): each item is also a list, meaning all entities from a paper
        """
        query = """
            UNWIND $hash_ids AS hash_id
            MATCH (e:Entity)-[:RELATED_TO]->(p:Paper {hash_id: hash_id})
            RETURN hash_id, e.name AS entity_name
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, hash_ids=hash_ids).data()
            )
        # 按照每个 hash_id 分组实体
        entity_list = []
        for record in result:
            entity_list.append(record["entity_name"])
        return entity_list

    def find_paper_by_entity(self, entity_name):
        """Find all papers with `entity_name`
        Args:
            entity_name (str)
        Returns:
            res (List of hash_ids): papers with `entity_name`
        """
        query = """
            MATCH (e1:Entity {name: $entity_name})-[:RELATED_TO]->(p:Paper)
            RETURN p.hash_id AS hash_id
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, entity_name=entity_name).data()
            )
        if result:
            return [record["hash_id"] for record in result]
        else:
            return []

    # TODO: @云翔
    # 增加通过entity返回包含entity语句的功能
    def find_sentence_by_entity(self, entity_name):
        # Return: list(str)
        return []

    def find_sentences_by_entity(self, entity_name):
        """Find all sentences with a certain `entity_name`
        Args:
            entity_name (str)
        Return:
            sentences (List of strs): One str concatenates all sentences with `entity_name` in a section
                E.g. [
                    "abstract sentence 1 from paper 1.abstract sentence 2 from paper 1",
                    "introduction sentence 1 from paper 1.introduction sentence 2 from paper 1",
                    "methodology sentence 1 from paper 1.",
                    "abstract sentence 1 from paper 2.abstract sentence 2 from paper 2",
                    ...
                ]
        """
        query = """
        MATCH (e:Entity {name: $entity_name})-[:RELATED_TO]->(p:Paper)
        WHERE p.abstract CONTAINS $entity_name OR
          p.introduction CONTAINS $entity_name OR
          p.methodology CONTAINS $entity_name
        RETURN p.abstract AS abstract, 
            p.introduction AS introduction, 
            p.methodology AS methodology, 
            p.hash_id AS hash_id
        """
        sentences = []

        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, entity_name=entity_name).data()
            )
        for record in result:
            for key in ["abstract", "introduction", "methodology"]:
                if record[key]:
                    filtered_sentences = [
                        sentence.strip() + "."
                        for sentence in record[key].split(".")
                        if entity_name in sentence
                    ]
                    sentences.extend(
                        [
                            f"{record['hash_id']}: {sentence}"
                            for sentence in filtered_sentences
                        ]
                    )

        return sentences

    def select_paper(self, venue_name, year):
        """Retrieve a list of papers which published at the `venue_name` in `year`
        Args:
            venue_name (str)
            year (int?)
        Returns:
            result (List of paper node)
        """
        query = """
            MATCH (n:Paper) where n.year=$year and n.venue_name=$venue_name return n
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, year=year, venue_name=venue_name).data()
            )
        if result:
            return [record["n"] for record in result]
        else:
            return []

    def add_paper_node(self, paper: dict):
        """Add a paper node
        Args:
            paper (Dict)
        Returns:
            None
        """
        if "summary" not in paper.keys():
            paper["summary"] = None
        if "abstract" not in paper.keys():
            paper["abstract"] = None
        if "introduction" not in paper.keys():
            paper["introduction"] = None
        if "reference" not in paper.keys():
            paper["reference"] = None
        if "cite" not in paper.keys():
            paper["cite"] = None
        if "motivation" not in paper.keys():
            paper["motivation"] = None
        if "contribution" not in paper.keys():
            paper["contribution"] = None
        if "methodology" not in paper.keys():
            paper["methodology"] = None
        if "ground_truth" not in paper.keys():
            paper["ground_truth"] = None
        if "reference_filter" not in paper.keys():
            paper["reference_filter"] = None
        if "conclusions" not in paper.keys():
            paper["conclusions"] = None
        query = """
            MERGE (p:Paper {hash_id: $hash_id})
            ON CREATE SET p.venue_name = $venue_name, p.year = $year, p.title = $title, p.pdf_url = $pdf_url, p.abstract = $abstract, p.introduction = $introduction, p.reference = $reference, p.summary = $summary, p.motivation = $motivation, p.contribution = $contribution, p.methodology = $methodology, p.ground_truth = $ground_truth, p.reference_filter = $reference_filter, p.conclusions = $conclusions
            ON MATCH SET p.venue_name = $venue_name, p.year = $year, p.title = $title, p.pdf_url = $pdf_url, p.abstract = $abstract, p.introduction = $introduction, p.reference = $reference, p.summary = $summary, p.motivation = $motivation, p.contribution = $contribution, p.methodology = $methodology, p.ground_truth = $ground_truth, p.reference_filter = $reference_filter, p.conclusions = $conclusions
            RETURN p
            """
        with self.driver.session() as session:
            result = session.execute_write(
                lambda tx: tx.run(
                    query,
                    hash_id=paper["hash_id"],
                    venue_name=paper["venue_name"],
                    year=paper["year"],
                    title=paper["title"],
                    pdf_url=paper["pdf_url"],
                    abstract=paper["abstract"],
                    introduction=paper["introduction"],
                    reference=paper["reference"],
                    summary=paper["summary"],
                    motivation=paper["motivation"],
                    contribution=paper["contribution"],
                    methodology=paper["methodology"],
                    ground_truth=paper["ground_truth"],
                    reference_filter=paper["reference_filter"],
                    conclusions=paper["conclusions"],
                ).data()
            )

    def check_entity_node_count(self, hash_id: int):
        """Whether a paper has more than `3` entities
        Args:
            hash_id: a paper's hash_id
        Returns:
            True if has <= 2 entitis, False otherwise
        """
        query_check_count = """
            MATCH (e:Entity)-[:RELATED_TO]->(p:Paper {hash_id: $hash_id})
            RETURN count(e) AS entity_count
        """
        with self.driver.session() as session:
            # Check the number of related entities
            result = session.execute_read(
                lambda tx: tx.run(query_check_count, hash_id=hash_id).data()
            )
            if result[0]["entity_count"] > 3:
                return False
        return True

    def add_entity_node(self, hash_id: int, entities: list):
        """Add a entity node, and link it to its paper
        Args:
            hash_id: a paper's id
            entities: a paper's all entities
        Returns:
            None
        """
        query = """
            MERGE (e:Entity {name: $entity_name})
            WITH e
            MATCH (p:Paper {hash_id: $hash_id})
            MERGE (e)-[:RELATED_TO]->(p)
            RETURN e, p
        """
        with self.driver.session() as session:
            for entity_name in entities:
                result = session.execute_write(
                    lambda tx: tx.run(
                        query, entity_name=entity_name, hash_id=hash_id
                    ).data()
                )

    def add_paper_citation(self, paper: dict):
        """Add citations for the paper node, set its cite_id_list, entities, and all_cite_id_list
        `cite_id_list` means citations in the Introduction section
        `all_cite_id_list` means all citations
        Args:
            paper (Dict of a paper)
        Returns:
            None
        """
        query = """
            MERGE (p:Paper {hash_id: $hash_id}) ON MATCH SET p.cite_id_list = $cite_id_list, p.entities = $entities, p.all_cite_id_list = $all_cite_id_list
            """
        with self.driver.session() as session:
            result = session.execute_write(
                lambda tx: tx.run(
                    query,
                    hash_id=paper["hash_id"],
                    cite_id_list=paper["cite_id_list"],
                    entities=paper["entities"],
                    all_cite_id_list=paper["all_cite_id_list"],
                ).data()
            )

    def insert_new_field(self, hash_id: str, field_name: str, content):
        if hash_id is not None:
            query = f"""
                MATCH (n {{hash_id: $hash_id}}) 
                SET n.{field_name} = $content
                RETURN n
            """
            with self.driver.session() as session:
                result = session.execute_write(
                    lambda tx: tx.run(
                        query, hash_id=hash_id, content=content
                    ).data()
                )
            return result
        else:
            return None

    def update_paper_embedding(
        self, embedding_model, hash_id=None, batch_size=512, name="abstract", postfix=""
    ):
        """Extract paper embedding and store in the database
        Args:
            embedding_model (TODO: what model?): an pytorch embedding model
            hash_id (str): add embedding for a paper if hash_id is not None.
                Otherwise, all papers will be handled with a batch size of 512
            batch_size: if hash_id is None, all papers will be processed with `batch_size`
        """
        if hash_id is not None:
            query = f"""
            MATCH (p:Paper {{hash_id: $hash_id}})
            WHERE p.{name} IS NOT NULL
            RETURN p.{name} AS context, p.hash_id AS hash_id, p.title AS title
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(query, hash_id=hash_id).data()
                )
            # contexts = [result["title"] + result["context"] for result in results]
            if name == "abstract":
                contexts = [result["title"] + result["context"] for result in results]
            else:
                contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts, convert_to_tensor=True, device=self.device
            )
            query = f"""
                MERGE (p:Paper {{hash_id: $hash_id}})
                ON CREATE SET p.{name}_embedding{postfix} = $embedding
                ON MATCH SET p.{name}_embedding{postfix} = $embedding
            """
            for idx, hash_id in tqdm(enumerate(paper_ids)):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                with self.driver.session() as session:
                    results = session.execute_write(
                        lambda tx: tx.run(
                            query, hash_id=hash_id, embedding=embedding
                        ).data()
                    )
            return
        offset = 0
        while True:
            query = f"""
            MATCH (p:Paper)
            WHERE p.{name} IS NOT NULL
            RETURN p.{name} AS context, p.hash_id AS hash_id, p.title AS title
            SKIP $offset LIMIT $batch_size
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(
                        query, offset=offset, batch_size=batch_size
                    ).data()
                )
            if not results:
                break
            if name == "abstract":
                contexts = [result["title"] + result["context"] for result in results]
            else:
                contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts,
                batch_size=batch_size,
                convert_to_tensor=True,
                device=self.device,
            )
            write_query = f"""
            UNWIND $data AS row
            MERGE (p:Paper {{hash_id: row.hash_id}})
            ON CREATE SET p.{name}_embedding{postfix} = row.embedding
            ON MATCH SET p.{name}_embedding{postfix} = row.embedding
            """
            data_to_write = []
            context_embeddings = context_embeddings.detach().cpu().tolist()
            for idx, hash_id in enumerate(paper_ids):
                data_to_write.append({"hash_id": hash_id, "embedding": context_embeddings[idx]})
            with self.driver.session() as session:
                session.execute_write(
                    lambda tx: tx.run(write_query, data=data_to_write)
                )
            offset += batch_size
            logger.info(f"== Processed batch starting at offset {offset} ==")

    def add_paper_abstract_embedding(
        self, embedding_model, hash_id=None, batch_size=512
    ):
        """Extract paper abstract embedding and store in the database
        Args:
            embedding_model (TODO: what model?): an pytorch embedding model
            hash_id (str): add abstract embedding for a paper if hash_id is not None.
                Otherwise, all papers will be handled with a batch size of 512
            batch_size: if hash_id is None, all papers will be processed with `batch_size`
        """
        if hash_id is not None:
            query = """
            MATCH (p:Paper {hash_id: $hash_id})
            WHERE p.abstract IS NOT NULL
            RETURN p.abstract AS context, p.hash_id AS hash_id, p.title AS title
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(query, hash_id=hash_id).data()
                )
            contexts = [result["title"] + result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts, convert_to_tensor=True, device=self.device
            )
            query = """
                MERGE (p:Paper {hash_id: $hash_id})
                ON CREATE SET p.abstract_embedding = $embedding
                ON MATCH SET p.abstract_embedding = $embedding
            """
            for idx, hash_id in tqdm(enumerate(paper_ids)):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                with self.driver.session() as session:
                    results = session.execute_write(
                        lambda tx: tx.run(
                            query, hash_id=hash_id, embedding=embedding
                        ).data()
                    )
            return
        offset = 0
        while True:
            query = f"""
            MATCH (p:Paper)
            WHERE p.abstract IS NOT NULL
            RETURN p.abstract AS context, p.hash_id AS hash_id, p.title AS title
            SKIP $offset LIMIT $batch_size
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(
                        query, offset=offset, batch_size=batch_size
                    ).data()
                )
            if not results:
                break
            contexts = [result["title"] + result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts,
                batch_size=batch_size,
                convert_to_tensor=True,
                device=self.device,
            )
            write_query = """
            UNWIND $data AS row
            MERGE (p:Paper {hash_id: row.hash_id})
            ON CREATE SET p.abstract_embedding = row.embedding
            ON MATCH SET p.abstract_embedding = row.embedding
            """
            data_to_write = []
            for idx, hash_id in enumerate(paper_ids):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                data_to_write.append({"hash_id": hash_id, "embedding": embedding})
            with self.driver.session() as session:
                session.execute_write(
                    lambda tx: tx.run(write_query, data=data_to_write)
                )
            offset += batch_size
            logger.info(f"== Processed batch starting at offset {offset} ==")

    def add_paper_bg_embedding(self, embedding_model, hash_id=None, batch_size=512):
        """Extract paper background embedding and store in the database
        Args:
            embedding_model (TODO: what model?): an pytorch embedding model
            hash_id (str): add background embedding for a paper if hash_id is not None.
                Otherwise, all papers will be handled with a batch size of 512
            batch_size: if hash_id is None, all papers will be processed with `batch_size`
        """
        if hash_id is not None:
            query = """
            MATCH (p:Paper {hash_id: $hash_id})
            WHERE p.motivation IS NOT NULL
            RETURN p.motivation AS context, p.hash_id AS hash_id, p.title AS title
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(query, hash_id=hash_id).data()
                )
            contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts, convert_to_tensor=True, device=self.device
            )
            query = """
                MERGE (p:Paper {hash_id: $hash_id})
                ON CREATE SET p.motivation_embedding = $embedding
                ON MATCH SET p.motivation_embedding = $embedding
            """
            for idx, hash_id in tqdm(enumerate(paper_ids)):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                with self.driver.session() as session:
                    results = session.execute_write(
                        lambda tx: tx.run(
                            query, hash_id=hash_id, embedding=embedding
                        ).data()
                    )
            return
        offset = 0
        while True:
            query = f"""
            MATCH (p:Paper)
            WHERE p.motivation IS NOT NULL
            RETURN p.motivation AS context, p.hash_id AS hash_id, p.title AS title
            SKIP $offset LIMIT $batch_size
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(
                        query, offset=offset, batch_size=batch_size
                    ).data()
                )
            if not results:
                break
            contexts = [result["title"] + result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts,
                batch_size=batch_size,
                convert_to_tensor=True,
                device=self.device,
            )
            write_query = """
            UNWIND $data AS row
            MERGE (p:Paper {hash_id: row.hash_id})
            ON CREATE SET p.motivation_embedding = row.embedding
            ON MATCH SET p.motivation_embedding = row.embedding
            """
            data_to_write = []
            for idx, hash_id in enumerate(paper_ids):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                data_to_write.append({"hash_id": hash_id, "embedding": embedding})
            with self.driver.session() as session:
                session.execute_write(
                    lambda tx: tx.run(write_query, data=data_to_write)
                )
            offset += batch_size
            logger.info(f"== Processed batch starting at offset {offset} ==")

    def add_paper_contribution_embedding(
        self, embedding_model, hash_id=None, batch_size=512
    ):
        """Extract paper contribution embedding and store in the database
        Args:
            embedding_model (TODO: what model?): an pytorch embedding model
            hash_id (str): add contribution embedding for a paper if hash_id is not None.
                Otherwise, all papers will be handled with a batch size of 512
            batch_size: if hash_id is None, all papers will be processed with `batch_size`
        """
        if hash_id is not None:
            query = """
            MATCH (p:Paper {hash_id: $hash_id})
            WHERE p.contribution IS NOT NULL
            RETURN p.contribution AS context, p.hash_id AS hash_id, p.title AS title
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(query, hash_id=hash_id).data()
                )
            contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts, convert_to_tensor=True, device=self.device
            )
            query = """
                MERGE (p:Paper {hash_id: $hash_id})
                ON CREATE SET p.contribution_embedding = $embedding
                ON MATCH SET p.contribution_embedding = $embedding
            """
            for idx, hash_id in tqdm(enumerate(paper_ids)):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                with self.driver.session() as session:
                    results = session.execute_write(
                        lambda tx: tx.run(
                            query, hash_id=hash_id, embedding=embedding
                        ).data()
                    )
            return
        offset = 0
        while True:
            query = f"""
            MATCH (p:Paper)
            WHERE p.contribution IS NOT NULL
            RETURN p.contribution AS context, p.hash_id AS hash_id, p.title AS title
            SKIP $offset LIMIT $batch_size
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(
                        query, offset=offset, batch_size=batch_size
                    ).data()
                )
            if not results:
                break
            contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts,
                batch_size=batch_size,
                convert_to_tensor=True,
                device=self.device,
            )
            write_query = """
            UNWIND $data AS row
            MERGE (p:Paper {hash_id: row.hash_id})
            ON CREATE SET p.contribution_embedding = row.embedding
            ON MATCH SET p.contribution_embedding = row.embedding
            """
            data_to_write = []
            for idx, hash_id in enumerate(paper_ids):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                data_to_write.append({"hash_id": hash_id, "embedding": embedding})
            with self.driver.session() as session:
                session.execute_write(
                    lambda tx: tx.run(write_query, data=data_to_write)
                )
            offset += batch_size
            logger.info(f"== Processed batch starting at offset {offset} ==")

    def add_paper_summary_embedding(
        self, embedding_model, hash_id=None, batch_size=512
    ):
        """Extract paper summary embedding and store in the database
        Args:
            embedding_model (TODO: what model?): an pytorch embedding model
            hash_id (str): add summary embedding for a paper if hash_id is not None.
                Otherwise, all papers will be handled with a batch size of 512
            batch_size: if hash_id is None, all papers will be processed with `batch_size`
        """
        if hash_id is not None:
            query = """
            MATCH (p:Paper {hash_id: $hash_id})
            WHERE p.summary IS NOT NULL
            RETURN p.summary AS context, p.hash_id AS hash_id, p.title AS title
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(query, hash_id=hash_id).data()
                )
            contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            # context_embeddings are pytorch.Tensor
            context_embeddings = embedding_model.encode(
                contexts, convert_to_tensor=True, device=self.device
            )
            query = """
                MERGE (p:Paper {hash_id: $hash_id})
                ON CREATE SET p.summary_embedding = $embedding
                ON MATCH SET p.summary_embedding = $embedding
            """
            for idx, hash_id in tqdm(enumerate(paper_ids)):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                with self.driver.session() as session:
                    results = session.execute_write(
                        lambda tx: tx.run(
                            query, hash_id=hash_id, embedding=embedding
                        ).data()
                    )
            return
        offset = 0
        while True:
            query = f"""
            MATCH (p:Paper)
            WHERE p.summary IS NOT NULL
            RETURN p.summary AS context, p.hash_id AS hash_id, p.title AS title
            SKIP $offset LIMIT $batch_size
            """
            with self.driver.session() as session:
                results = session.execute_write(
                    lambda tx: tx.run(
                        query, offset=offset, batch_size=batch_size
                    ).data()
                )
            if not results:
                break
            contexts = [result["context"] for result in results]
            paper_ids = [result["hash_id"] for result in results]
            context_embeddings = embedding_model.encode(
                contexts,
                batch_size=batch_size,
                convert_to_tensor=True,
                device=self.device,
            )
            write_query = """
            UNWIND $data AS row
            MERGE (p:Paper {hash_id: row.hash_id})
            ON CREATE SET p.summary_embedding = row.embedding
            ON MATCH SET p.summary_embedding = row.embedding
            """
            data_to_write = []
            for idx, hash_id in enumerate(paper_ids):
                embedding = (
                    context_embeddings[idx].detach().cpu().numpy().flatten().tolist()
                )
                data_to_write.append({"hash_id": hash_id, "embedding": embedding})
            with self.driver.session() as session:
                session.execute_write(
                    lambda tx: tx.run(write_query, data=data_to_write)
                )
            offset += batch_size
            logger.info(f"== Processed batch starting at offset {offset} ==")

    def cosine_similarity_search(self, embedding, k=1, type_name="embedding"):
        """Retrieve all papers whose `type_name` embedding is similar to `embedding`
        (cosine_sim > 0 and return in a descending order)
        Args:
            embedding (TODO: type): the embedding to be checked
            k: only return topk papers with highest similarities
            type_name: "abstract_embedding", "summary_embedding", etc.
        Returns:
            related_paper (List of str): hash_id of retrieved papers
        """
        query = f"""
            MATCH (paper:Paper)
            WITH paper,
                vector.similarity.cosine(paper.{type_name}, $embedding) AS score
            WHERE score > 0
            RETURN paper, score
            ORDER BY score DESC LIMIT {k}
            """
        with self.driver.session() as session:
            results = session.execute_read(
                lambda tx: tx.run(query, embedding=embedding).data()
            )
        related_paper = []
        for result in results:
            related_paper.append(result["paper"]["hash_id"])
        return related_paper

    def create_vector_index(self):
        """
        适用于Paper节点，这里的语句应该是针对所有数据库里的paper都做索引
        针对Paper节点上的是属性 embedding 进行索引
        索引向量的维度为384
        适用余弦相似度作为计算相似度的方法
        """
        query = """
            CREATE VECTOR INDEX `paper-embeddings`
            FOR (n:Paper) ON (n.embedding)
            OPTIONS {indexConfig: {
            `vector.dimensions`: 384,
            `vector.similarity_function`: 'cosine'
            }}
            """
        with self.driver.session() as session:
            session.execute_write(lambda tx: tx.run(query).data())

    def filter_paper_id_list(self, paper_id_list, year="2024"):
        """Retrieve all papers' ids which released before "year" (not contained) and existed in the database
        Args:
            paper_id_list (List of str): a list of paper ids
            year: the paper before
        Returns:
            existing_paper_ids (List of str): paper_ids that satisfy the conditions
        """
        if not paper_id_list:
            return []
        # WHERE p.year < "2024" AND p.venue_name <> "acl"
        query = """
            UNWIND $paper_id_list AS hash_id
            MATCH (p:Paper {hash_id: hash_id})
            WHERE p.year < $year
            RETURN p.hash_id AS hash_id
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, paper_id_list=paper_id_list, year=year).data()
            )

        existing_paper_ids = [record["hash_id"] for record in result]
        existing_paper_ids = list(set(existing_paper_ids))
        return existing_paper_ids

    def check_index_exists(self):
        query = "SHOW INDEXES"
        with self.driver.session() as session:
            result = session.execute_read(lambda tx: tx.run(query).data())
        for record in result:
            if record["name"] == "paper-embeddings":
                return True
        return False

    def clear_database(self):
        query = """
        MATCH (n)
        DETACH DELETE n
        """
        with self.driver.session() as session:
            session.execute_write(lambda tx: tx.run(query).data())

    def get_entity_related_paper_num(self, entity_name):
        query = """
            MATCH (e:Entity {name: $entity_name})-[:RELATED_TO]->(p:Paper)
            WITH COUNT(p) AS PaperCount
            RETURN PaperCount
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, entity_name=entity_name).data()
            )
        paper_num = result[0]["PaperCount"]
        return paper_num

    def get_entities_related_paper_num(self, entity_names):
        query = """
            UNWIND $entity_names AS entity_name
            MATCH (e:Entity {name: entity_name})-[:RELATED_TO]->(p:Paper)
            WITH entity_name, COUNT(p) AS PaperCount
            RETURN entity_name, PaperCount
        """

        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, entity_names=entity_names).data()
            )
        # 将查询结果转化为字典形式：实体名称 -> 论文数量
        entity_paper_count = {
            record["entity_name"]: record["PaperCount"] for record in result
        }
        return entity_paper_count

    def get_entity_text(self):
        query = """
            MATCH (e:Entity)-[:RELATED_TO]->(p:Paper)
            WHERE p.venue_name = $venue_name and p.year = $year
            WITH p, collect(e.name) AS entity_names
            RETURN p, reduce(text = '', name IN entity_names | text + ' ' + name) AS entity_text
        """
        with self.driver.session() as session:
            result = session.execute_read(lambda tx: tx.run(query).data())
        text_list = [record["entity_text"] for record in result]
        return text_list

    def get_entity_combinations(self, venue_name, year):
        def process_paper_relationships(
            session, entity_name_1, entity_name_2, abstract
        ):
            if entity_name_2 < entity_name_1:
                entity_name_1, entity_name_2 = entity_name_2, entity_name_1
            query = """
                MATCH (e1:Entity {name: $entity_name_1})
                MATCH (e2:Entity {name: $entity_name_2})
                MERGE (e1)-[r:CONNECT]->(e2)
                ON CREATE SET r.strength = 1
                ON MATCH SET r.strength = r.strength + 1
            """
            sentences = re.split(r"(?<!\w\.\w.)(?<![A-Z][a-z]\.)(?<=\.|\?)\s", abstract)
            for sentence in sentences:
                sentence = sentence.lower()
                if entity_name_1 in sentence and entity_name_2 in sentence:
                    # 如果两个实体在同一句话中出现过，则创建或更新 CONNECT 关系
                    session.execute_write(
                        lambda tx: tx.run(
                            query,
                            entity_name_1=entity_name_1,
                            entity_name_2=entity_name_2,
                        ).data()
                    )
                    # logger.debug(f"CONNECT relation created or updated between {entity_name_1} and {entity_name_2} for Paper ID {paper_id}")
                    break  # 如果找到一次出现就可以退出循环

        query = """
            MATCH (e:Entity)-[:RELATED_TO]->(p:Paper)
            WHERE p.venue_name=$venue_name and p.year=$year
            WITH p, collect(e) as entities
            UNWIND range(0, size(entities)-2) as i
            UNWIND range(i+1, size(entities)-1) as j
            RETURN p.hash_id AS hash_id, entities[i].name AS entity_name_1, entities[j].name AS entity_name_2
        """
        with self.driver.session() as session:
            result = session.execute_read(
                lambda tx: tx.run(query, venue_name=venue_name, year=year).data()
            )
            for record in tqdm(result):
                paper_id = record["hash_id"]
                entity_name_1 = record["entity_name_1"]
                entity_name_2 = record["entity_name_2"]
                abstract = self.get_paper_attribute(paper_id, "abstract")
                process_paper_relationships(
                    session, entity_name_1, entity_name_2, abstract
                )

    def build_citemap(self):
        citemap = defaultdict(set)
        query = """
            MATCH (p:Paper)
            RETURN p.hash_id AS hash_id, p.cite_id_list AS cite_id_list
        """
        with self.driver.session() as session:
            results = session.execute_read(lambda tx: tx.run(query).data())
        for result in results:
            hash_id = result["hash_id"]
            cite_id_list = result["cite_id_list"]
            if cite_id_list:
                for cited_id in cite_id_list:
                    citemap[hash_id].add(cited_id)
        return citemap

    def neo4j_backup(self):
        URI = os.environ["NEO4J_URL"]
        NEO4J_USERNAME = os.environ["NEO4J_USERNAME"]
        NEO4J_PASSWD = os.environ["NEO4J_PASSWD"]
        AUTH = (NEO4J_USERNAME, NEO4J_PASSWD)
        graph = Graph(URI, auth=AUTH)
        # 创建一个字典来保存数据
        # 定义批次大小
        data = {"nodes": [], "relationships": []}
        # 计算数据的总数（例如查询节点总数）
        total_papers_query = "MATCH (e:Entity)-[:RELATED_TO]->(p:Paper) RETURN COUNT(DISTINCT p) AS count"
        total_papers = graph.run(total_papers_query).evaluate()
        print(f"total paper: {total_papers}")
        query = f"""
            MATCH (e:Entity)-[r:RELATED_TO]->(p:Paper)
            RETURN p, e, r
        """
        """
        results = graph.run(query)
        # 处理查询结果
        for record in tqdm(results):
            paper_node = record["p"]
            entity_node = record["e"]
            relationship = record["r"]
            # 将节点数据加入字典
            data["nodes"].append(
                {
                    "id": paper_node.identity,
                    "label": "Paper",
                    "properties": dict(paper_node),
                }
            )
            data["nodes"].append(
                {
                    "id": entity_node.identity,
                    "label": "Entity",
                    "properties": dict(entity_node),
                }
            )
            # 将关系数据加入字典
            data["relationships"].append(
                {
                    "start_node": entity_node.identity,
                    "end_node": paper_node.identity,
                    "type": "RELATED_TO",
                    "properties": dict(relationship),
                }
            )
        """
        query = """
            MATCH (p:Paper)
            WHERE p.venue_name='acl' and p.year='2024'
            RETURN p
        """
        results = graph.run(query)
        for record in tqdm(results):
            paper_node = record["p"]
            # 将节点数据加入字典
            data["nodes"].append(
                {
                    "id": paper_node.identity,
                    "label": "Paper",
                    "properties": dict(paper_node),
                }
            )
        # 去除重复节点
        # data["nodes"] = [dict(t) for t in {tuple(d.items()) for d in data["nodes"]}]
        unique_nodes = []
        seen = set()
        for node in tqdm(data["nodes"]):
            # 将字典项转换为不可变的元组，以便用于集合去重
            node_tuple = str(tuple(sorted(node.items())))
            if node_tuple not in seen:
                seen.add(node_tuple)
                unique_nodes.append(node)
        data["nodes"] = unique_nodes
        # 将数据保存为 JSON 文件
        with open(
            "./assets/data/scipip_neo4j_clean_backup.json", "w", encoding="utf-8"
        ) as f:
            json.dump(data, f, ensure_ascii=False, indent=4)

    def neo4j_import_data(self):
        # clear_database() # 清空数据库，谨慎执行
        URI = os.environ["NEO4J_URL"]
        NEO4J_USERNAME = os.environ["NEO4J_USERNAME"]
        NEO4J_PASSWD = os.environ["NEO4J_PASSWD"]
        AUTH = (NEO4J_USERNAME, NEO4J_PASSWD)
        graph = Graph(URI, auth=AUTH)
        # 从 JSON 文件中读取数据
        with open(
            "./assets/data/scipip_neo4j_clean_backup.json", "r", encoding="utf-8"
        ) as f:
            data = json.load(f)
        # 创建节点
        nodes = {}
        for node_data in data["nodes"]:
            label = node_data["label"]
            properties = node_data["properties"]
            node = Node(label, **properties)
            graph.create(node)
            nodes[node_data["id"]] = node

        # 创建关系
        for relationship_data in data["relationships"]:
            start_node = nodes[relationship_data["start_node"]]
            end_node = nodes[relationship_data["end_node"]]
            properties = relationship_data["properties"]
            rel_type = relationship_data["type"]
            relationship = Relationship(start_node, rel_type, end_node, **properties)
            graph.create(relationship)

    def get_paper_by_id(self, hash_id):
        paper = {"hash_id": hash_id}
        self.update_paper_from_client(paper)
        return paper


if __name__ == "__main__":
    paper_client = PaperClient()
    # paper_client.neo4j_backup()
    paper_client.neo4j_import_data()