This commit is contained in:
2025-09-07 12:59:40 +08:00
parent d9c954a06e
commit 1ba6d0a7f3
2 changed files with 1 additions and 113 deletions

View File

@@ -16,6 +16,7 @@ from volcenginesdkarkruntime import Ark
from Config.Config import VOLC_SECRETKEY, VOLC_API_KEY, VOLC_ACCESSKEY
from Volcengine.VikingDBMemoryService import MEMORY_COLLECTION_NAME, VikingDBMemoryException, VikingDBMemoryService, \
initialize_services, ensure_collection_exists
from Volcengine.chat import handle_conversation_turn, archive_conversation
# 控制日志输出
logger = logging.getLogger('CollectionMemory')
@@ -29,119 +30,6 @@ if not logger.handlers:
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
def main():
logger.info("开始端到端记忆测试...")

Binary file not shown.