class GraphRag(object):    def __init__(self):        """Any embedding function implementing `langchain.embeddings.base.Embeddings` interface."""        self._database = 'neo4j'        self.label = 'Med'        self._driver = neo4j.GraphDatabase.driver(            uri=os.environ["NEO4J_URI"],            auth=(os.environ["NEO4J_USERNAME"],                  os.environ["NEO4J_PASSWORD"]))        self.embeddings_zh = HuggingFaceEmbeddings(model_name=os.environ["EMBEDDING_MODEL"])        self.vectstore = Neo4jVector(embedding=self.embeddings_zh,                                     username=os.environ["NEO4J_USERNAME"],                                     password=os.environ["NEO4J_PASSWORD"],                                     url=os.environ["NEO4J_URI"],                                     node_label=self.label,                                     index_name="vector"                                     )

    def query(self, query: str, params: dict = {}) -> List[Dict[str, Any]]:        """Query Neo4j database."""        from neo4j.exceptions import CypherSyntaxError
        with self._driver.session(database=self._database) as session:            try:                data = session.run(query, params)                return [r.data() for r in data]            except CypherSyntaxError as e:                raise ValueError(f"Generated Cypher Statement is not valid\n{e}")
    def add_embeddings(self):        """Add embeddings to Neo4j database."""        # 查询图中所有节点，并且根据节点的描述和名字生成embedding，添加到该节点上        query = """MATCH (n) WHERE not (n:{}) RETURN ID(n) AS id, labels(n) as labels, n""".format(self.label)        print("qurey node...")        data = self.query(query)        ids, texts, embeddings, metas = [], [], [], []        for row in tqdm(data,desc="parsing node"):            ids.append(row['id'])            text = row['n'].get('name','') + row['n'].get('desc','')            texts.append(text)            metas.append({"label": row['labels'], "context": text})        self.embeddings_zh.multi_process = False
        print("node embeddings...")        embeddings = self.embeddings_zh.embed_documents(texts)        print("adding node embeddings...")        ids_ret = self.vectstore.add_embeddings(            ids=ids,            texts=texts,            embeddings=embeddings,            metadatas=metas        )        return ids_ret
    # Fulltext index query    def structured_retriever(self, query, limit=3, simlarity=0.9) -> str:        """        Collects the neighborhood of entities mentioned in the question        """        # step1 从图谱中检索与查询相关的实体。        docs_with_score = self.vectstore.similarity_search_with_score(query, k=topk)        entities = [item[0].page_content for item in data if item[1] > simlarity] # score        self.vectstore.query(            "CREATE FULLTEXT INDEX entity IF NOT EXISTS FOR (e:Med) ON EACH [e.context]")        result = ""        for entity in entities:            qry = entity            # step2 从全局索引中查出entity label，            query1 =  f"""CALL db.index.fulltext.queryNodes('entity', '{qry}') YIELD node, score                             return node.label as label,node.context as context, node.id as id, score LIMIT {limit}"""            data1 = self.vectstore.query(query1)            # step3 根据label在相应的节点中查询邻居节点路径            for item in data1:                node_type = item['label']                node_type = item['label'] if type(node_type) == str else node_type[0]                node_id = item['id']                query2 = f"""match (node:{node_type})-[r]-(neighbor) where ID(node) = {node_id} RETURN type(r) as rel, node.name+' - '+type(r)+' - '+neighbor.name as output limit 50"""                data2 = self.vectstore.query(query2)                # step4 为了保持多样性，对关系进行筛选                rel_dict = defaultdict(list)                if len(data2) > 3*limit:                    for item1 in data2:                        rel_dict[item1['rel']].append(item1['output'])                if rel_dict:                    rel_dict = {k:random.sample(v, 3) if len(v)>3 else v for k,v in rel_dict.items()}                    result += "\n".join(['\n'.join(el) for el in rel_dict.values()]) +'\n'                else:                    result += "\n".join([el['output'] for el in data2]) +'\n'        return result