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248
dsLightRag/Volcengine/T3_ChatWithMemory.py
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248
dsLightRag/Volcengine/T3_ChatWithMemory.py
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import logging
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import sys
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import time
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import json
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from Config.Config import VOLC_ACCESSKEY, VOLC_SECRETKEY, VOLC_API_KEY
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from Volcengine.Kit.VikingDBMemoryService import VikingDBMemoryService, MEMORY_COLLECTION_NAME
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from volcenginesdkarkruntime import Ark
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# 控制日志输出
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logger = logging.getLogger('ChatWithMemory')
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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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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"
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)
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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 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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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],
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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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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 handle_conversation_turn(memory_service, llm_client, collection_name, 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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relevant_memories = search_relevant_memories(memory_service, 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(memory_service, collection_name, 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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memory_service.add_session(
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collection_name=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 main():
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logger.info("开始测试大模型记忆功能...")
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try:
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# 使用initialize_services函数初始化服务和LLM客户端
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memory_service, llm_client = initialize_services()
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collection_name = MEMORY_COLLECTION_NAME
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user_id = "liming"
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assistant_id = "assistant"
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# 告知大模型用户信息
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logger.info("告知大模型用户信息...")
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user_info = "李明，男生，15岁，家住长春"
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# 记录信息到记忆体
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logger.info("记录用户信息到记忆体...")
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# 准备对话历史
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conversation_history = []
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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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memory_service=memory_service,
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llm_client=llm_client,
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collection_name=collection_name,
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user_id=user_id,
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user_message=f"请记住以下用户信息：{user_info}",
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conversation_history=conversation_history
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)
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logger.info(f"模型回复: {response}")
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# 归档对话
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archive_conversation(
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memory_service=memory_service,
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collection_name=collection_name,
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user_id=user_id,
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assistant_id=assistant_id,
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conversation_history=conversation_history,
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topic_name="user_info"
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)
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# 等待一段时间确保索引更新
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logger.info("等待索引更新...")
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time.sleep(5)
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# 验证大模型是否记住个人信息
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logger.info("验证大模型是否记住个人信息...")
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test_conversation_history = []
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test_response = handle_conversation_turn(
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memory_service=memory_service,
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llm_client=llm_client,
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collection_name=collection_name,
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user_id=user_id,
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user_message="请告诉我李明的个人信息",
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conversation_history=test_conversation_history
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)
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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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keywords = ["李明", "男", "15", "长春"]
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found_keywords = [kw for kw in keywords if kw in test_response]
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if len(found_keywords) == len(keywords):
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logger.info("✅ 大模型成功记住了用户信息！")
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else:
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logger.info(f"❌ 大模型可能没有完全记住用户信息。找到的关键词: {found_keywords}")
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# 尝试直接搜索记忆
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logger.info("尝试直接搜索记忆...")
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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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search_result = memory_service.search_memory(
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collection_name=collection_name,
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query="李明 15岁 长春 男生",
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filter=filter_params,
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limit=5
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)
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logger.info(f"搜索结果: {search_result}")
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except Exception as e:
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logger.error(f"操作失败: {e}")
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sys.exit(1)
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if __name__ == "__main__":
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main()
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