554 lines
24 KiB
Python
554 lines
24 KiB
Python
"""
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pip install volcengine
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pip install --upgrade "volcengine-python-sdk[ark]"
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"""
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import json
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import logging
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import threading
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import time
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from dotenv import load_dotenv
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from volcengine.ApiInfo import ApiInfo
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from volcengine.Credentials import Credentials
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from volcengine.ServiceInfo import ServiceInfo
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from volcengine.auth.SignerV4 import SignerV4
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from volcengine.base.Service import Service
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from volcenginesdkarkruntime import Ark
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from Config.Config import VOLC_SECRETKEY, VOLC_ACCESSKEY, VOLC_API_KEY
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# 配置日志
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logger = logging.getLogger('CollectionMemory')
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logger.setLevel(logging.INFO)
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# 只添加一次处理器，避免重复日志
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if not logger.handlers:
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handler = logging.StreamHandler()
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handler.setFormatter(logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s'))
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logger.addHandler(handler)
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# 记忆体集合名称
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MEMORY_COLLECTION_NAME="dsideal_collection"
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class VikingDBMemoryException(Exception):
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def __init__(self, code, request_id, message=None):
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self.code = code
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self.request_id = request_id
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self.message = "{}, code:{}，request_id:{}".format(message, self.code, self.request_id)
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def __str__(self):
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return self.message
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class VikingDBMemoryService(Service):
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_instance_lock = threading.Lock()
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def __new__(cls, *args, **kwargs):
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if not hasattr(VikingDBMemoryService, "_instance"):
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with VikingDBMemoryService._instance_lock:
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if not hasattr(VikingDBMemoryService, "_instance"):
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VikingDBMemoryService._instance = object.__new__(cls)
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return VikingDBMemoryService._instance
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def __init__(self, host="api-knowledgebase.mlp.cn-beijing.volces.com", region="cn-beijing", ak="", sk="",
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sts_token="", scheme='https',
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connection_timeout=30, socket_timeout=30):
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self.service_info = VikingDBMemoryService.get_service_info(host, region, scheme, connection_timeout,
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socket_timeout)
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self.api_info = VikingDBMemoryService.get_api_info()
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super(VikingDBMemoryService, self).__init__(self.service_info, self.api_info)
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if ak:
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self.set_ak(ak)
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if sk:
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self.set_sk(sk)
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if sts_token:
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self.set_session_token(session_token=sts_token)
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try:
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self.get_body("Ping", {}, json.dumps({}))
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except Exception as e:
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raise VikingDBMemoryException(1000028, "missed", "host or region is incorrect".format(str(e))) from None
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def setHeader(self, header):
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api_info = VikingDBMemoryService.get_api_info()
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for key in api_info:
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for item in header:
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api_info[key].header[item] = header[item]
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self.api_info = api_info
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@staticmethod
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def get_service_info(host, region, scheme, connection_timeout, socket_timeout):
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service_info = ServiceInfo(host, {"Host": host},
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Credentials('', '', 'air', region), connection_timeout, socket_timeout,
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scheme=scheme)
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return service_info
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@staticmethod
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def get_api_info():
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api_info = {
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"CreateCollection": ApiInfo("POST", "/api/memory/collection/create", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"GetCollection": ApiInfo("POST", "/api/memory/collection/info", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"DropCollection": ApiInfo("POST", "/api/memory/collection/delete", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"UpdateCollection": ApiInfo("POST", "/api/memory/collection/update", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"SearchMemory": ApiInfo("POST", "/api/memory/search", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"AddSession": ApiInfo("POST", "/api/memory/session/add", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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"Ping": ApiInfo("GET", "/api/memory/ping", {}, {},
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{'Accept': 'application/json', 'Content-Type': 'application/json'}),
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}
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return api_info
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def get_body(self, api, params, body):
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if not (api in self.api_info):
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raise Exception("no such api")
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api_info = self.api_info[api]
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r = self.prepare_request(api_info, params)
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r.headers['Content-Type'] = 'application/json'
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r.headers['Traffic-Source'] = 'SDK'
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r.body = body
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SignerV4.sign(r, self.service_info.credentials)
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url = r.build()
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resp = self.session.get(url, headers=r.headers, data=r.body,
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timeout=(self.service_info.connection_timeout, self.service_info.socket_timeout))
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if resp.status_code == 200:
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return json.dumps(resp.json())
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else:
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raise Exception(resp.text.encode("utf-8"))
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def get_body_exception(self, api, params, body):
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try:
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res = self.get_body(api, params, body)
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except Exception as e:
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try:
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res_json = json.loads(e.args[0].decode("utf-8"))
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except:
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raise VikingDBMemoryException(1000028, "missed", "json load res error, res:{}".format(str(e))) from None
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code = res_json.get("code", 1000028)
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request_id = res_json.get("request_id", 1000028)
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message = res_json.get("message", None)
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raise VikingDBMemoryException(code, request_id, message)
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if res == '':
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raise VikingDBMemoryException(1000028, "missed",
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"empty response due to unknown error, please contact customer service") from None
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return res
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def get_exception(self, api, params):
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try:
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res = self.get(api, params)
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except Exception as e:
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try:
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res_json = json.loads(e.args[0].decode("utf-8"))
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except:
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raise VikingDBMemoryException(1000028, "missed", "json load res error, res:{}".format(str(e))) from None
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code = res_json.get("code", 1000028)
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request_id = res_json.get("request_id", 1000028)
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message = res_json.get("message", None)
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raise VikingDBMemoryException(code, request_id, message)
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if res == '':
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raise VikingDBMemoryException(1000028, "missed",
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"empty response due to unknown error, please contact customer service") from None
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return res
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def create_collection(self, collection_name, description="", custom_event_type_schemas=[],
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custom_entity_type_schemas=[], builtin_event_types=[], builtin_entity_types=[]):
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params = {
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"CollectionName": collection_name, "Description": description,
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"CustomEventTypeSchemas": custom_event_type_schemas, "CustomEntityTypeSchemas": custom_entity_type_schemas,
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"BuiltinEventTypes": builtin_event_types, "BuiltinEntityTypes": builtin_entity_types,
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}
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res = self.json("CreateCollection", {}, json.dumps(params))
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return json.loads(res)
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def get_collection(self, collection_name):
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params = {"CollectionName": collection_name}
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res = self.json("GetCollection", {}, json.dumps(params))
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return json.loads(res)
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def drop_collection(self, collection_name):
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params = {"CollectionName": collection_name}
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res = self.json("DropCollection", {}, json.dumps(params))
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return json.loads(res)
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def update_collection(self, collection_name, custom_event_type_schemas=[], custom_entity_type_schemas=[],
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builtin_event_types=[], builtin_entity_types=[]):
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params = {
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"CollectionName": collection_name,
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"CustomEventTypeSchemas": custom_event_type_schemas, "CustomEntityTypeSchemas": custom_entity_type_schemas,
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"BuiltinEventTypes": builtin_event_types, "BuiltinEntityTypes": builtin_entity_types,
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}
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res = self.json("UpdateCollection", {}, json.dumps(params))
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return json.loads(res)
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def search_memory(self, collection_name, query, filter, limit=10):
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params = {
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"collection_name": collection_name,
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"query": query,
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"limit": limit,
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"filter": filter,
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}
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res = self.json("SearchMemory", {}, json.dumps(params))
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return json.loads(res)
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def add_session(self, collection_name, session_id, messages, metadata, entities=None):
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params = {
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"collection_name": collection_name,
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"session_id": session_id,
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"messages": messages,
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"metadata": metadata,
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}
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if entities is not None:
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params["entities"] = entities
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res = self.json("AddSession", {}, json.dumps(params))
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return json.loads(res)
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def handle_conversation_turn(self, llm_client, user_id, user_message, conversation_history):
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"""处理一轮对话，包括记忆搜索和LLM响应。"""
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logger.info("\n" + "=" * 60)
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logger.info(f"用户: {user_message}")
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# 修复：调用正确的search_relevant_memories方法
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relevant_memories = self.search_relevant_memories(MEMORY_COLLECTION_NAME, user_id, user_message)
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system_prompt = "你是一个富有同情心、善于倾听的AI伙伴，拥有长期记忆能力。你的目标是为用户提供情感支持和温暖的陪伴。"
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if relevant_memories:
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memory_context = "\n".join(
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[f"- {json.dumps(mem['memory_info'], ensure_ascii=False)}" for mem in relevant_memories])
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system_prompt += f"\n\n这是我们过去的一些对话记忆，请参考：\n{memory_context}\n\n请利用这些信息来更好地理解和回应用户。"
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logger.info("AI正在思考...")
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try:
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messages = [{"role": "system", "content": system_prompt}] + conversation_history + [
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{"role": "user", "content": user_message}]
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completion = llm_client.chat.completions.create(
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model="doubao-seed-1-6-flash-250715",
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messages=messages
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)
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assistant_reply = completion.choices[0].message.content
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except Exception as e:
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logger.info(f"LLM调用失败: {e}")
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assistant_reply = "抱歉，我现在有点混乱，无法回应。我们可以稍后再聊吗？"
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logger.info(f"伙伴: {assistant_reply}")
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conversation_history.extend([
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{"role": "user", "content": user_message},
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{"role": "assistant", "content": assistant_reply}
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])
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return assistant_reply
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def archive_conversation(self, user_id, assistant_id, conversation_history, topic_name):
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"""将对话历史归档到记忆数据库。"""
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if not conversation_history:
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logger.info("没有对话可以归档。")
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return False
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logger.info(f"\n正在归档关于 '{topic_name}' 的对话...")
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session_id = f"{topic_name}_{int(time.time())}"
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metadata = {
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"default_user_id": user_id,
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"default_assistant_id": assistant_id,
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"time": int(time.time() * 1000)
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}
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try:
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self.add_session(
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collection_name=MEMORY_COLLECTION_NAME,
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session_id=session_id,
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messages=conversation_history,
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metadata=metadata
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)
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logger.info(f"对话已成功归档，会话ID: {session_id}")
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logger.info("正在等待记忆索引更新...")
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return True
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except Exception as e:
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logger.info(f"归档对话失败: {e}")
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return False
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def wait_for_collection_ready(self, timeout=300, interval=10):
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"""
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等待集合准备就绪
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:param timeout: 超时时间(秒)
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:param interval: 检查间隔(秒)
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:return: True if ready, False if timeout
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"""
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start_time = time.time()
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while time.time() - start_time < timeout:
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try:
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collection_info = self.get_collection(MEMORY_COLLECTION_NAME)
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# 打印完整集合信息用于调试
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logger.info(f"集合详细信息: {json.dumps(collection_info, ensure_ascii=False)}")
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# 尝试多种可能的状态字段名
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status = collection_info.get("Status") or collection_info.get("status") or "UNKNOWN"
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logger.info(f"集合 '{MEMORY_COLLECTION_NAME}' 当前状态: {status}")
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# 检查是否为就绪状态（可能的值：READY, RUNNING, ACTIVE等）
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if status in ["READY", "RUNNING", "ACTIVE"]:
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return True
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time.sleep(interval)
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except Exception as e:
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logger.error(f"检查集合状态失败: {str(e)}")
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time.sleep(interval)
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logger.error(f"集合 '{MEMORY_COLLECTION_NAME}' 在{timeout}秒内未就绪")
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return False
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def initialize_services(self, ak=None, sk=None, ark_api_key=None):
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"""初始化记忆数据库服务和LLM客户端"""
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load_dotenv()
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# 如果参数未提供，尝试从环境变量获取
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if not ak:
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ak = VOLC_ACCESSKEY
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if not sk:
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sk = VOLC_SECRETKEY
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if not ark_api_key:
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ark_api_key = VOLC_API_KEY
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if not all([ak, sk, ark_api_key]):
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raise ValueError("必须提供 VOLC_ACCESSKEY, VOLC_SECRETKEY, 和 VOLC_API_KEY。")
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memory_service = VikingDBMemoryService(ak=ak, sk=sk)
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llm_client = Ark(
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base_url="https://ark.cn-beijing.volces.com/api/v3",
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api_key=ark_api_key,
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)
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return memory_service, llm_client
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def ensure_collection_exists(self, collection_name, description="",
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builtin_event_types=["sys_event_v1", "sys_profile_collect_v1"],
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builtin_entity_types=["sys_profile_v1"]):
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"""检查记忆集合是否存在，如果不存在则创建。"""
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try:
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self.get_collection(collection_name)
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logger.info(f"记忆集合 '{collection_name}' 已存在。")
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except Exception as e:
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error_message = str(e)
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if "collection not exist" in error_message:
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logger.info(f"记忆集合 '{collection_name}' 未找到，正在创建...")
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try:
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self.create_collection(
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collection_name=collection_name,
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description=description,
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builtin_event_types=builtin_event_types,
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builtin_entity_types=builtin_entity_types
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)
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logger.info(f"记忆集合 '{collection_name}' 创建成功。")
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logger.info("等待集合准备就绪...")
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except Exception as create_e:
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logger.info(f"创建集合失败: {create_e}")
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raise
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else:
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logger.info(f"检查集合时出错: {e}")
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raise
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def search_relevant_memories(self, collection_name, user_id, query, limit=3):
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"""搜索与用户查询相关的记忆，并在索引构建中时重试。"""
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logger.info(f"正在搜索与 '{query}' 相关的记忆...")
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retry_attempt = 0
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while True:
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try:
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filter_params = {
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"user_id": user_id, # 修正为字符串类型
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"memory_type": ["sys_event_v1", "sys_profile_v1"]
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}
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response = self.search_memory(
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collection_name=collection_name,
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query=query,
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filter=filter_params,
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limit=limit
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)
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memories = []
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if response.get('data', {}).get('count', 0) > 0:
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for result in response['data']['result_list']:
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if 'memory_info' in result and result['memory_info']:
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memories.append({
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'memory_info': result['memory_info'],
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'score': result['score']
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})
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if memories:
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if retry_attempt > 0:
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logger.info("重试后搜索成功。")
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logger.info(f"找到 {len(memories)} 条相关记忆:")
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for i, memory in enumerate(memories, 1):
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logger.info(
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f" {i}. (相关度: {memory['score']:.3f}): {json.dumps(memory['memory_info'], ensure_ascii=False, indent=2)}")
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else:
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logger.info("未找到相关记忆。")
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return memories
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except Exception as e:
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error_message = str(e)
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if "1000023" in error_message:
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retry_attempt += 1
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logger.info(f"记忆索引正在构建中。将在60秒后重试... (尝试次数 {retry_attempt})")
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time.sleep(60)
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else:
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logger.info(f"搜索记忆时出错 (不可重试): {e}")
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return []
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def setup_memory_collection(self):
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"""独立封装记忆体创建逻辑，返回memory_service供测试使用"""
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try:
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self.ensure_collection_exists(MEMORY_COLLECTION_NAME)
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logger.info(f"记忆体 '{MEMORY_COLLECTION_NAME}' 创建/验证成功")
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# 添加集合就绪等待
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logger.info("等待集合准备就绪...")
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if self.wait_for_collection_ready():
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logger.info(f"集合 '{MEMORY_COLLECTION_NAME}' 已就绪")
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return self
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else:
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logger.info(f"集合 '{MEMORY_COLLECTION_NAME}' 未能就绪")
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return None
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except Exception as e:
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logger.info(f"记忆体创建失败: {e}")
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return None
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def initialize_services():
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"""初始化服务和LLM客户端"""
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ak = VOLC_ACCESSKEY
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sk = VOLC_SECRETKEY
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ark_api_key = VOLC_API_KEY
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if not all([ak, sk, ark_api_key]):
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raise ValueError("必须在环境变量中设置 VOLC_ACCESSKEY, VOLC_SECRETKEY, 和 ARK_API_KEY。")
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memory_service = VikingDBMemoryService(
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ak=ak,
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sk=sk,
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host="api-knowledgebase.mlp.cn-beijing.volces.com",
|
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region="cn-beijing"
|
||
)
|
||
llm_client = Ark(
|
||
base_url="https://ark.cn-beijing.volces.com/api/v3",
|
||
api_key=ark_api_key,
|
||
)
|
||
return memory_service, llm_client
|
||
|
||
|
||
def search_relevant_memories(memory_service, collection_name, user_id, query):
|
||
"""搜索与用户查询相关的记忆，并在索引构建中时重试。"""
|
||
logger.info(f"正在搜索与 '{query}' 相关的记忆...")
|
||
retry_attempt = 0
|
||
while True:
|
||
try:
|
||
filter_params = {
|
||
"user_id": [user_id],
|
||
"memory_type": ["sys_event_v1", "sys_profile_v1"]
|
||
}
|
||
response = memory_service.search_memory(
|
||
collection_name=collection_name,
|
||
query=query,
|
||
filter=filter_params,
|
||
limit=3
|
||
)
|
||
|
||
memories = []
|
||
if response.get('data', {}).get('count', 0) > 0:
|
||
for result in response['data']['result_list']:
|
||
if 'memory_info' in result and result['memory_info']:
|
||
memories.append({
|
||
'memory_info': result['memory_info'],
|
||
'score': result['score']
|
||
})
|
||
|
||
if memories:
|
||
if retry_attempt > 0:
|
||
logger.info("重试后搜索成功。")
|
||
logger.info(f"找到 {len(memories)} 条相关记忆:")
|
||
for i, memory in enumerate(memories, 1):
|
||
logger.info(
|
||
f" {i}. (相关度: {memory['score']:.3f}): {json.dumps(memory['memory_info'], ensure_ascii=False, indent=2)}")
|
||
else:
|
||
logger.info("未找到相关记忆。")
|
||
return memories
|
||
|
||
except Exception as e:
|
||
error_message = str(e)
|
||
if "1000023" in error_message:
|
||
retry_attempt += 1
|
||
logger.info(f"记忆索引正在构建中。将在60秒后重试... (尝试次数 {retry_attempt})")
|
||
time.sleep(60)
|
||
else:
|
||
logger.info(f"搜索记忆时出错 (不可重试): {e}")
|
||
return []
|
||
|
||
|
||
def handle_conversation_turn(memory_service, llm_client, collection_name, user_id, user_message, conversation_history):
|
||
"""处理一轮对话，包括记忆搜索和LLM响应。"""
|
||
logger.info("\n" + "=" * 60)
|
||
logger.info(f"用户: {user_message}")
|
||
|
||
relevant_memories = search_relevant_memories(memory_service, collection_name, user_id, user_message)
|
||
|
||
system_prompt = "你是一个富有同情心、善于倾听的AI伙伴，拥有长期记忆能力。你的目标是为用户提供情感支持和温暖的陪伴。"
|
||
if relevant_memories:
|
||
memory_context = "\n".join(
|
||
[f"- {json.dumps(mem['memory_info'], ensure_ascii=False)}" for mem in relevant_memories])
|
||
system_prompt += f"\n\n这是我们过去的一些对话记忆，请参考：\n{memory_context}\n\n请利用这些信息来更好地理解和回应用户。"
|
||
|
||
logger.info("AI正在思考...")
|
||
|
||
try:
|
||
messages = [{"role": "system", "content": system_prompt}] + conversation_history + [
|
||
{"role": "user", "content": user_message}]
|
||
completion = llm_client.chat.completions.create(
|
||
model="doubao-seed-1-6-flash-250715",
|
||
messages=messages
|
||
)
|
||
assistant_reply = completion.choices[0].message.content
|
||
except Exception as e:
|
||
logger.info(f"LLM调用失败: {e}")
|
||
assistant_reply = "抱歉，我现在有点混乱，无法回应。我们可以稍后再聊吗？"
|
||
|
||
logger.info(f"伙伴: {assistant_reply}")
|
||
|
||
conversation_history.extend([
|
||
{"role": "user", "content": user_message},
|
||
{"role": "assistant", "content": assistant_reply}
|
||
])
|
||
return assistant_reply
|
||
|
||
|
||
def archive_conversation(memory_service, collection_name, user_id, assistant_id, conversation_history, topic_name):
|
||
"""将对话历史归档到记忆数据库。"""
|
||
if not conversation_history:
|
||
logger.info("没有对话可以归档。")
|
||
return False
|
||
|
||
logger.info(f"\n正在归档关于 '{topic_name}' 的对话...")
|
||
session_id = f"{topic_name}_{int(time.time())}"
|
||
metadata = {
|
||
"default_user_id": user_id,
|
||
"default_assistant_id": assistant_id,
|
||
"time": int(time.time() * 1000)
|
||
}
|
||
|
||
try:
|
||
memory_service.add_session(
|
||
collection_name=collection_name,
|
||
session_id=session_id,
|
||
messages=conversation_history,
|
||
metadata=metadata
|
||
)
|
||
logger.info(f"对话已成功归档，会话ID: {session_id}")
|
||
logger.info("正在等待记忆索引更新...")
|
||
return True
|
||
except Exception as e:
|
||
logger.info(f"归档对话失败: {e}")
|
||
return False |