189 lines
7.1 KiB
Python
189 lines
7.1 KiB
Python
import logging
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import logging
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import threading
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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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import json
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import time
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from dotenv import load_dotenv
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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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from Volcengine.VikingDBMemoryService import MEMORY_COLLECTION_NAME, VikingDBMemoryException, VikingDBMemoryService, \
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initialize_services, ensure_collection_exists
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# 控制日志输出
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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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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("开始端到端记忆测试...")
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try:
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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 = "system"
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assistant_id = "assistant"
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ensure_collection_exists(memory_service, collection_name)
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except Exception as e:
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logger.info(f"初始化失败: {e}")
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return
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logger.info("\n--- 阶段 1: 初始对话 ---")
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initial_conversation_history = []
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handle_conversation_turn(
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memory_service, llm_client, collection_name, user_id,
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"你好，我是小明，今年18岁，但压力好大。",
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initial_conversation_history
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)
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handle_conversation_turn(
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memory_service, llm_client, collection_name, user_id,
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"马上就要高考了，家里人的期待好高。",
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initial_conversation_history
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)
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logger.info("\n--- 阶段 2: 归档记忆 ---")
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archive_conversation(
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memory_service, collection_name, user_id, assistant_id,
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initial_conversation_history, "study_stress_discussion"
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)
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logger.info("\n--- 阶段 3: 验证记忆 ---")
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verification_conversation_history = []
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handle_conversation_turn(
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memory_service, llm_client, collection_name, user_id,
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"我最近很焦虑，不知道该怎么办。",
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verification_conversation_history
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)
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logger.info("\n端到端记忆测试完成！")
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if __name__ == "__main__":
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main() |