'commit'
This commit is contained in:
@@ -42,25 +42,25 @@ def main():
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# 使用正确的handle_conversation_turn方法参数
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# 使用正确的handle_conversation_turn方法参数
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response = handle_conversation_turn(
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response = handle_conversation_turn(
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memory_service=memory_service,# 内存记忆服务
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memory_service=memory_service, # 内存记忆服务
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llm_client=llm_client,# 大模型客户端
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llm_client=llm_client, # 大模型客户端
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collection_name=collection_name,# 集合名称
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collection_name=collection_name, # 集合名称
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user_id=user_id,# 用户ID
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user_id=user_id, # 用户ID
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assistant_id=assistant_id,# 助手ID
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assistant_id=assistant_id, # 助手ID
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user_message=f"请记住以下用户信息：{user_info}",# 要记忆的信息
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user_message=f"请记住以下用户信息：{user_info}", # 要记忆的信息
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conversation_history=conversation_history # 对话历史
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conversation_history=conversation_history # 对话历史
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)
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)
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logger.info(f"模型回复: {response}")
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logger.info(f"模型回复: {response}")
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# 归档对话
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# 归档对话
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archive_conversation(
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archive_conversation(
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memory_service=memory_service,
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memory_service=memory_service, # 内存记忆服务
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collection_name=collection_name,
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collection_name=collection_name, # 集合名称
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user_id=user_id,
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user_id=user_id, # 用户ID
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assistant_id=assistant_id,
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assistant_id=assistant_id, # 助手ID
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conversation_history=conversation_history,
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conversation_history=conversation_history, # 对话历史
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topic_name="user_info"
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topic_name="user_info" # 会话主题
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)
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)
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# 等待一段时间确保索引更新
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# 等待一段时间确保索引更新
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@@ -72,15 +72,16 @@ def main():
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test_conversation_history = []
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test_conversation_history = []
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test_response = handle_conversation_turn(
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test_response = handle_conversation_turn(
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memory_service=memory_service,
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memory_service=memory_service, # 内存记忆服务
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llm_client=llm_client,
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llm_client=llm_client,
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collection_name=collection_name,
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collection_name=collection_name,# 集合名称
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user_id=user_id,
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user_id=user_id, # 用户ID
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assistant_id=assistant_id, # 添加这一行
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assistant_id=assistant_id, # 助手ID
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user_message="请告诉我李明的个人信息",
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user_message="请告诉我李明的个人信息",# 交流的信息
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conversation_history=test_conversation_history
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conversation_history=test_conversation_history # 对话历史
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)
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)
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# 这次交互就没有记录信息，看来需要根据业务来决定是不是记录到记忆体中
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logger.info(f"测试回复: {test_response}")
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logger.info(f"测试回复: {test_response}")
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# 检查回复中是否包含关键信息
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# 检查回复中是否包含关键信息
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@@ -1,399 +0,0 @@
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import json
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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_API_KEY, VOLC_ACCESSKEY
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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 initialize_services():
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load_dotenv()
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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(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(memory_service, collection_name):
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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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memory_service.get_collection(collection_name)
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print(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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print(f"记忆集合 '{collection_name}' 未找到，正在创建...")
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try:
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memory_service.create_collection(
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collection_name=collection_name,
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description="中文情感陪伴场景测试",
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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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)
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print(f"记忆集合 '{collection_name}' 创建成功。")
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print("等待集合准备就绪...")
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except Exception as create_e:
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print(f"创建集合失败: {create_e}")
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raise
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else:
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print(f"检查集合时出错: {e}")
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raise
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def search_relevant_memories(memory_service, collection_name, user_id, query):
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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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print(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],
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"memory_type": ["sys_event_v1", "sys_profile_v1"]
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}
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response = memory_service.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=3
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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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print("重试后搜索成功。")
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print(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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print(
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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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print("未找到相关记忆。")
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return memories
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|
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except Exception as e:
|
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error_message = str(e)
|
|
||||||
if "1000023" in error_message:
|
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retry_attempt += 1
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print(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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|
||||||
print(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。"""
|
|
||||||
print("\n" + "=" * 60)
|
|
||||||
print(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。"
|
|
||||||
|
|
||||||
print("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:
|
|
||||||
print(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？"
|
|
||||||
|
|
||||||
print(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:
|
|
||||||
print("没有对话可以归档。")
|
|
||||||
return False
|
|
||||||
|
|
||||||
print(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
|
|
||||||
)
|
|
||||||
print(f"对话已成功归档，会话ID: {session_id}")
|
|
||||||
print("正在等待记忆索引更新...")
|
|
||||||
return True
|
|
||||||
except Exception as e:
|
|
||||||
print(f"归档对话失败: {e}")
|
|
||||||
return False
|
|
||||||
|
|
||||||
|
|
||||||
def main():
|
|
||||||
print("开始端到端记忆测试...")
|
|
||||||
|
|
||||||
try:
|
|
||||||
memory_service, llm_client = initialize_services()
|
|
||||||
collection_name = "emotional_support"
|
|
||||||
user_id = "xiaoming"
|
|
||||||
assistant_id = "assistant"
|
|
||||||
ensure_collection_exists(memory_service, collection_name)
|
|
||||||
except Exception as e:
|
|
||||||
print(f"初始化失败: {e}")
|
|
||||||
return
|
|
||||||
|
|
||||||
print("\n--- 阶段 1: 初始对话 ---")
|
|
||||||
initial_conversation_history = []
|
|
||||||
handle_conversation_turn(
|
|
||||||
memory_service, llm_client, collection_name, user_id,
|
|
||||||
"你好，我是小明，今年18岁，但压力好大。",
|
|
||||||
initial_conversation_history
|
|
||||||
)
|
|
||||||
handle_conversation_turn(
|
|
||||||
memory_service, llm_client, collection_name, user_id,
|
|
||||||
"马上就要高考了，家里人的期待好高。",
|
|
||||||
initial_conversation_history
|
|
||||||
)
|
|
||||||
|
|
||||||
print("\n--- 阶段 2: 归档记忆 ---")
|
|
||||||
archive_conversation(
|
|
||||||
memory_service, collection_name, user_id, assistant_id,
|
|
||||||
initial_conversation_history, "study_stress_discussion"
|
|
||||||
)
|
|
||||||
|
|
||||||
print("\n--- 阶段 3: 验证记忆 ---")
|
|
||||||
verification_conversation_history = []
|
|
||||||
handle_conversation_turn(
|
|
||||||
memory_service, llm_client, collection_name, user_id,
|
|
||||||
"我最近很焦虑，不知道该怎么办。",
|
|
||||||
verification_conversation_history
|
|
||||||
)
|
|
||||||
|
|
||||||
print("\n端到端记忆测试完成！")
|
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
|
||||||
main()
|
|
||||||
Reference in New Issue
Block a user