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Merge branch 'dev' of https://github.com/MoFox-Studio/MoFox_Bot into dev

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This commit is contained in:
minecraft1024a
2025-10-25 21:24:58 +08:00
parent ee7a37ce70 162cb8dba6
commit ad9f06b85a
5 changed files with 69 additions and 32 deletions
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57
src/chat/memory_system/memory_system.py
View File

@@ -138,6 +138,7 @@ class MemorySystem:
self.config = config or MemorySystemConfig.from_global_config()
self.llm_model = llm_model
self.status = MemorySystemStatus.INITIALIZING
logger.info(f"MemorySystem __init__ called, id: {id(self)}")
# 核心组件（简化版）
self.memory_builder: MemoryBuilder | None = None
@@ -170,6 +171,7 @@ class MemorySystem:
async def initialize(self):
"""异步初始化记忆系统"""
logger.info(f"MemorySystem initialize started, id: {id(self)}")
try:
# 初始化LLM模型
fallback_task = getattr(self.llm_model, "model_for_task", None) if self.llm_model else None
@@ -222,8 +224,13 @@ class MemorySystem:
)
try:
self.unified_storage = VectorMemoryStorage(storage_config)
logger.info("✅ Vector DB存储系统初始化成功")
try:
self.unified_storage = VectorMemoryStorage(storage_config)
logger.info("✅ Vector DB存储系统初始化成功")
except Exception as storage_error:
logger.error(f"❌ Vector DB存储系统初始化失败: {storage_error}", exc_info=True)
self.unified_storage = None # 确保在失败时为None
raise
except Exception as storage_error:
logger.error(f"❌ Vector DB存储系统初始化失败: {storage_error}", exc_info=True)
raise
@@ -282,7 +289,7 @@ class MemorySystem:
# 统一存储已经自动加载数据，无需额外加载
self.status = MemorySystemStatus.READY
logger.info(f"MemorySystem initialize finished, id: {id(self)}")
except Exception as e:
self.status = MemorySystemStatus.ERROR
logger.error(f"❌ 记忆系统初始化失败: {e}", exc_info=True)
@@ -405,6 +412,8 @@ class MemorySystem:
logger.debug(f"海马体采样模式：使用价值评分 {value_score:.2f}")
# 2. 构建记忆块（所有记忆统一使用 global 作用域，实现完全共享）
if not self.memory_builder:
raise RuntimeError("Memory builder is not initialized.")
memory_chunks = await self.memory_builder.build_memories(
conversation_text,
normalized_context,
@@ -419,6 +428,8 @@ class MemorySystem:
# 3. 记忆融合与去重（包含与历史记忆的融合）
existing_candidates = await self._collect_fusion_candidates(memory_chunks)
if not self.fusion_engine:
raise RuntimeError("Fusion engine is not initialized.")
fused_chunks = await self.fusion_engine.fuse_memories(memory_chunks, existing_candidates)
# 4. 存储记忆到统一存储
@@ -537,7 +548,12 @@ class MemorySystem:
if isinstance(result, Exception):
logger.warning("融合候选向量搜索失败: %s", result)
continue
for memory_id, similarity in result:
if not result or not isinstance(result, list):
continue
for item in result:
if not isinstance(item, tuple) or len(item) != 2:
continue
memory_id, similarity = item
if memory_id in new_memory_ids:
continue
if similarity is None or similarity < min_threshold:
@@ -810,7 +826,11 @@ class MemorySystem:
importance_score = (importance_enum.value - 1) / 3.0
else:
# 如果已经是数值，直接使用
importance_score = float(importance_enum) if importance_enum else 0.5
importance_score = (
float(importance_enum.value)
if hasattr(importance_enum, "value")
else (float(importance_enum) if isinstance(importance_enum, int) else 0.5)
)
# 4. 访问频率得分（归一化，访问10次以上得满分）
access_count = memory.metadata.access_count
@@ -1395,6 +1415,9 @@ class MemorySystem:
}}
"""
if not self.value_assessment_model:
logger.warning("Value assessment model is not initialized, returning default value.")
return 0.5
response, _ = await self.value_assessment_model.generate_response_async(prompt, temperature=0.3)
# 解析响应
@@ -1488,10 +1511,11 @@ class MemorySystem:
def _populate_memory_fingerprints(self) -> None:
"""基于当前缓存构建记忆指纹映射"""
self._memory_fingerprints.clear()
for memory in self.unified_storage.memory_cache.values():
fingerprint = self._build_memory_fingerprint(memory)
key = self._fingerprint_key(memory.user_id, fingerprint)
self._memory_fingerprints[key] = memory.memory_id
if self.unified_storage:
for memory in self.unified_storage.memory_cache.values():
fingerprint = self._build_memory_fingerprint(memory)
key = self._fingerprint_key(memory.user_id, fingerprint)
self._memory_fingerprints[key] = memory.memory_id
def _register_memory_fingerprints(self, memories: list[MemoryChunk]) -> None:
for memory in memories:
@@ -1573,7 +1597,7 @@ class MemorySystem:
# 保存存储数据
if self.unified_storage:
await self.unified_storage.save_storage()
pass
# 记忆融合引擎维护
if self.fusion_engine:
@@ -1653,7 +1677,7 @@ class MemorySystem:
"""重建向量存储（如果需要）"""
try:
# 检查是否有记忆缓存数据
if not hasattr(self.unified_storage, "memory_cache") or not self.unified_storage.memory_cache:
if not self.unified_storage or not hasattr(self.unified_storage, "memory_cache") or not self.unified_storage.memory_cache:
logger.info("无记忆缓存数据，跳过向量存储重建")
return
@@ -1682,7 +1706,8 @@ class MemorySystem:
for i in range(0, len(memories_to_rebuild), batch_size):
batch = memories_to_rebuild[i : i + batch_size]
try:
await self.unified_storage.store_memories(batch)
if self.unified_storage:
await self.unified_storage.store_memories(batch)
rebuild_count += len(batch)
if rebuild_count % 50 == 0:
@@ -1705,22 +1730,28 @@ class MemorySystem:
# 全局记忆系统实例
memory_system: MemorySystem = None
memory_system: MemorySystem | None = None
def get_memory_system() -> MemorySystem:
"""获取全局记忆系统实例"""
global memory_system
if memory_system is None:
logger.warning("Global memory_system is None. Creating new uninitialized instance. This might be a problem.")
memory_system = MemorySystem()
logger.info(f"get_memory_system() called, returning instance with id: {id(memory_system)}")
return memory_system
async def initialize_memory_system(llm_model: LLMRequest | None = None):
"""初始化全局记忆系统"""
global memory_system
logger.info("initialize_memory_system() called.")
if memory_system is None:
logger.info("Global memory_system is None, creating new instance for initialization.")
memory_system = MemorySystem(llm_model=llm_model)
else:
logger.info(f"Global memory_system already exists (id: {id(memory_system)}). Initializing it.")
await memory_system.initialize()
# 根据配置启动海马体采样

1
src/common/vector_db/chromadb_impl.py
View File

@@ -46,6 +46,7 @@ class ChromaDBImpl(VectorDBBase):
logger.error(f"ChromaDB 初始化失败: {e}")
self.client = None
self._initialized = False
raise ConnectionError(f"ChromaDB 初始化失败: {e}") from e
def get_or_create_collection(self, name: str, **kwargs: Any) -> Any:
if not self.client:

4
src/main.py
View File

@@ -446,7 +446,9 @@ MoFox_Bot(第三方修改版)
# 初始化增强记忆系统
if global_config.memory.enable_memory:
await self._safe_init("增强记忆系统", self.memory_manager.initialize)()
from src.chat.memory_system.memory_system import initialize_memory_system
await self._safe_init("增强记忆系统", initialize_memory_system)()
await self._safe_init("记忆管理器", self.memory_manager.initialize)()
else:
logger.info("记忆系统已禁用，跳过初始化")

14
src/plugin_system/apis/tool_api.py
View File

@@ -1,3 +1,4 @@
from typing import Any
from src.common.logger import get_logger
from src.plugin_system.base.base_tool import BaseTool
from src.plugin_system.base.component_types import ComponentType
@@ -20,13 +21,22 @@ def get_tool_instance(tool_name: str) -> BaseTool | None:
return tool_class(plugin_config) if tool_class else None
def get_llm_available_tool_definitions():
def get_llm_available_tool_definitions() -> list[dict[str, Any]]:
"""获取LLM可用的工具定义列表
Returns:
List[Tuple[str, Dict[str, Any]]]: 工具定义列表，为[("tool_name", 定义)]
list[dict[str, Any]]: 工具定义列表
"""
from src.plugin_system.core import component_registry
llm_available_tools = component_registry.get_llm_available_tools()
tool_definitions = []
for tool_name, tool_class in llm_available_tools.items():
try:
# 调用类方法 get_tool_definition 获取定义
definition = tool_class.get_tool_definition()
tool_definitions.append(definition)
except Exception as e:
logger.error(f"获取工具 {tool_name} 的定义失败: {e}")
return tool_definitions

25
src/plugin_system/core/tool_use.py
View File

@@ -113,10 +113,14 @@ class ToolExecutor:
logger.debug(f"{self.log_prefix}开始LLM工具调用分析")
# 调用LLM进行工具决策
response, (reasoning_content, model_name, tool_calls) = await self.llm_model.generate_response_async(
response, llm_extra_info = await self.llm_model.generate_response_async(
prompt=prompt, tools=tools, raise_when_empty=False
)
tool_calls = None
if llm_extra_info and isinstance(llm_extra_info, tuple) and len(llm_extra_info) == 3:
_, _, tool_calls = llm_extra_info
# 执行工具调用
tool_results, used_tools = await self.execute_tool_calls(tool_calls)
@@ -133,7 +137,9 @@ class ToolExecutor:
user_disabled_tools = global_announcement_manager.get_disabled_chat_tools(self.chat_id)
# 获取基础工具定义（包括二步工具的第一步）
tool_definitions = [definition for name, definition in all_tools if name not in user_disabled_tools]
tool_definitions = [
definition for definition in all_tools if definition.get("function", {}).get("name") not in user_disabled_tools
]
# 检查是否有待处理的二步工具第二步调用
pending_step_two = getattr(self, "_pending_step_two_tools", {})
@@ -282,20 +288,7 @@ class ToolExecutor:
)
# 检查是否是MCP工具
try:
from src.plugin_system.utils.mcp_tool_provider import mcp_tool_provider
if function_name in mcp_tool_provider.mcp_tools:
logger.info(f"{self.log_prefix}执行MCP工具: {function_name}")
result = await mcp_tool_provider.call_mcp_tool(function_name, function_args)
return {
"tool_call_id": tool_call.call_id,
"role": "tool",
"name": function_name,
"type": "function",
"content": result.get("content", ""),
}
except Exception as e:
logger.debug(f"检查MCP工具时出错: {e}")
pass
function_args["llm_called"] = True # 标记为LLM调用