Python py2neo.Relationship() Examples

def insert_one_data(arr):
    start = look_and_create(arr[0])
    items = [arr[2]] if isinstance(arr[2], str) else arr[2]
    for name in items:
        end = look_and_create(name)
        r = Relationship(start, arr[1], end, name=arr[1])   
        graph.create(r) 

def insertAiroData(self, data):
        print("Inserting node data!")
        bssidNodes, stationNodes = data[0][0], data[0][1]
        for b in bssidNodes:
            try:
                bNode = Node(b['type'], name=b['name'], bssid=b['bssid'], oui=b['oui'], encryption=b["encryption"], speed=b['speed'], channel=b['channel'], auth=b['auth'], cipher=b['cipher'], lan=b['lan'])
                bNode.add_label("Device")
                self.graph.create(bNode)
            except ClientError:
                pass
        
        for essids, s in stationNodes:
            sNode = self.graph.nodes.match("Device", bssid=s['bssid']).first()
            if sNode is None:
                sNode = Node(s["type"], name=s['name'], bssid=s['bssid'], FirstTimeSeen=s['fts'], LastTimeSeen=s['lts'],Power=s['pwr'], NumPackets=s['pkts'], Association=s['assoc'], oui=s['oui'])
                sNode.add_label("Device")
            else:
                sNode['FirstTimeSeen'] = s['fts']
                sNode['LastTimeSeen'] = s['lts']
                sNode['Power'] = s['pwr']
                sNode['NumPackets'] = s['pkts']
                sNode['Association'] =s['assoc']
                self.graph.push(sNode)
                sNode = self.graph.nodes.match("Device", bssid=s['bssid']).first()

            for essid in essids: 
                nExisting = self.graph.nodes.match("Device", name=essid).first()
                if len(essid) > 0:
                    newProbe = Node("AP", name=essid)
                    newProbe.add_label("Device")
                    self.graph.create(Relationship(sNode, "Probes", nExisting or newProbe))
            
            if s['assoc'] is not None:
                aExisting = self.graph.nodes.match("Device", bssid=s['assoc']).first()
                newAssoc = Node("AP", bssid=s['assoc'])
                newAssoc.add_label("Device")
                self.graph.create(Relationship(sNode, "AssociatedTo", aExisting or newAssoc))
        
        print("Database updated!") 

def add_to_memory(self, question="question", userid="A0001"):
        """Add user question to memory.
        将用户当前对话加入信息记忆。

        Args:
            question: 用户问题。
                Defaults to "question".
            userid: 用户唯一标识。
                Defaults to "userid".
        """
        previous_node = self.graph.find_one("Memory", "qa_id", self.qa_id)
        self.qa_id = get_current_time()
        node = Node("Memory", question=question, userid=userid, qa_id=self.qa_id)
        if previous_node:
            relation = Relationship(previous_node, "next", node)
            self.graph.create(relation)
        else:
            self.graph.create(node)

    # def extract_navigation(self, question):
        """Extract navigation from question。从问题中抽取导航地点。
        从导航地点列表选取与问题匹配度最高的地点。
        QA匹配模式：（模糊匹配/全匹配）

        Args:
            question: User question. 用户问题。
        """
        # result = dict(question=question, name='', content=self.iformat(random_item(self.do_not_know)), \
            # context="", tid="", ftid="", url="", behavior=0, parameter="", txt="", img="", button="", valid=1)
        
        # 模式1：模糊匹配
        # temp_sim = 0
        # sv1 = synonym_cut(question, 'wf')
        # if not sv1:
            # return result
        # for location in self.locations:
            # sv2 = synonym_cut(location, 'wf')
            # if sv2:
                # temp_sim = similarity(sv1, sv2, 'j')
            # 匹配加速，不必选取最高相似度，只要达到阈值就终止匹配
            # if temp_sim > 0.92:
                # print("Navigation location: " + location + " Similarity Score: " + str(temp_sim))
                # result["content"] = location
                # result["context"] = "user_navigation"
                # result["behavior"] = int("0x001B", 16)
                # return result
        
        # 模式2：全匹配，判断“去”和地址关键词是就近的动词短语情况
        # for location in self.locations:
            
            # keyword = "去" + location
            # if keyword in question:
                # print("Original navigation")
                # result["name"] = keyword
                # result["content"] = location
                # result["context"] = "user_navigation"
                # result["behavior"] = int("0x001B", 16)
                # return result
        # return result