feat:实现具有向量和元数据索引的统一内存存储系统

- 添加了 UnifiedMemoryStorage 类,用于管理带向量嵌入的内存块。
- 集成了 FAISS,以实现高效的向量存储和搜索。
- 实现了内存缓存、关键字、类型和用户索引。
- 增加了内存遗忘和自动保存存储数据的支持。
- 包含用于存储、搜索和遗忘记忆的方法。
- 引入了存储行为和性能的配置选项。
- 实现了从磁盘加载和保存内存及向量数据。
This commit is contained in:
Windpicker-owo
2025-10-01 18:02:42 +08:00
parent d46475ca8c
commit afb1a75ebf
28 changed files with 1883 additions and 499 deletions

View File

@@ -604,16 +604,18 @@ class ChatterPlanFilter:
else:
keywords.append("晚上")
# 使用新的增强记忆系统检索记忆
# 使用新的统一记忆系统检索记忆
try:
from src.chat.memory_system.enhanced_memory_integration import recall_memories
from src.chat.memory_system import get_memory_system
memory_system = get_memory_system()
# 将关键词转换为查询字符串
query = " ".join(keywords)
enhanced_memories = await recall_memories(
query=query,
enhanced_memories = await memory_system.retrieve_relevant_memories(
query_text=query,
user_id="system", # 系统查询
chat_id="system"
scope_id="system",
limit=5
)
if not enhanced_memories:
@@ -621,9 +623,10 @@ class ChatterPlanFilter:
# 转换格式以兼容现有代码
retrieved_memories = []
if enhanced_memories and enhanced_memories.get("has_memories"):
for memory in enhanced_memories.get("memories", []):
retrieved_memories.append((memory.get("type", "unknown"), memory.get("content", "")))
for memory_chunk in enhanced_memories:
content = memory_chunk.display or memory_chunk.text_content or ""
memory_type = memory_chunk.memory_type.value if memory_chunk.memory_type else "unknown"
retrieved_memories.append((memory_type, content))
memory_statements = [f"关于'{topic}', 你记得'{memory_item}'" for topic, memory_item in retrieved_memories]

View File

@@ -349,7 +349,7 @@ class RemindAction(BaseAction):
# === 基本信息 ===
action_name = "set_reminder"
action_description = "根据用户的对话内容,智能地设置一个未来的提醒事项。"
activation_type = (ActionActivationType.KEYWORD,)
activation_type = ActionActivationType.KEYWORD
activation_keywords = ["提醒", "叫我", "记得", "别忘了"]
chat_type_allow = ChatType.ALL
parallel_action = True