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修复代码格式和文件名大小写问题

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This commit is contained in:
Windpicker-owo
2025-08-31 20:50:17 +08:00
parent df29014e41
commit 8149731925
218 changed files with 6913 additions and 8257 deletions
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70
src/chat/memory_system/Hippocampus.py
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@@ -16,7 +16,7 @@ from rich.traceback import install
from src.llm_models.utils_model import LLMRequest
from src.config.config import global_config, model_config
from sqlalchemy import select,insert,update,delete
from sqlalchemy import select, insert, update, delete
from src.common.database.sqlalchemy_models import Messages, GraphNodes, GraphEdges # SQLAlchemy Models导入
from src.common.logger import get_logger
from src.common.database.sqlalchemy_database_api import get_db_session
@@ -31,6 +31,7 @@ from src.chat.utils.utils import translate_timestamp_to_human_readable
install(extra_lines=3)
def calculate_information_content(text):
"""计算文本的信息量（熵）"""
char_count = Counter(text)
@@ -695,7 +696,9 @@ class Hippocampus:
return result
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> tuple[float, list[str]]:
async def get_activate_from_text(
self, text: str, max_depth: int = 3, fast_retrieval: bool = False
) -> tuple[float, list[str]]:
"""从文本中提取关键词并获取相关记忆。
Args:
@@ -863,10 +866,10 @@ class EntorhinalCortex:
current_memorized_times = message.get("memorized_times", 0)
with get_db_session() as session:
session.execute(
update(Messages)
.where(Messages.message_id == message["message_id"])
.values(memorized_times=current_memorized_times + 1)
)
update(Messages)
.where(Messages.message_id == message["message_id"])
.values(memorized_times=current_memorized_times + 1)
)
session.commit()
return messages # 直接返回原始的消息列表
@@ -951,7 +954,6 @@ class EntorhinalCortex:
for i in range(0, len(nodes_to_create), batch_size):
batch = nodes_to_create[i : i + batch_size]
session.execute(insert(GraphNodes), batch)
if nodes_to_update:
batch_size = 100
@@ -963,11 +965,9 @@ class EntorhinalCortex:
.where(GraphNodes.concept == node_data["concept"])
.values(**{k: v for k, v in node_data.items() if k != "concept"})
)
if nodes_to_delete:
session.execute(delete(GraphNodes).where(GraphNodes.concept.in_(nodes_to_delete)))
# 处理边的信息
db_edges = list(session.execute(select(GraphEdges)).scalars())
@@ -1023,7 +1023,6 @@ class EntorhinalCortex:
for i in range(0, len(edges_to_create), batch_size):
batch = edges_to_create[i : i + batch_size]
session.execute(insert(GraphEdges), batch)
if edges_to_update:
batch_size = 100
@@ -1037,7 +1036,6 @@ class EntorhinalCortex:
)
.values(**{k: v for k, v in edge_data.items() if k not in ["source", "target"]})
)
if edges_to_delete:
for source, target in edges_to_delete:
@@ -1048,12 +1046,10 @@ class EntorhinalCortex:
# 提交事务
session.commit()
end_time = time.time()
logger.info(f"[同步] 总耗时: {end_time - start_time:.2f}秒")
logger.info(f"[同步] 同步了 {len(memory_nodes)} 个节点和 {len(memory_edges)} 条边")
async def resync_memory_to_db(self):
"""清空数据库并重新同步所有记忆数据"""
start_time = time.time()
@@ -1064,7 +1060,7 @@ class EntorhinalCortex:
clear_start = time.time()
session.execute(delete(GraphNodes))
session.execute(delete(GraphEdges))
clear_end = time.time()
logger.info(f"[数据库] 清空数据库耗时: {clear_end - clear_start:.2f}秒")
@@ -1122,7 +1118,7 @@ class EntorhinalCortex:
for i in range(0, len(nodes_data), batch_size):
batch = nodes_data[i : i + batch_size]
session.execute(insert(GraphNodes), batch)
node_end = time.time()
logger.info(f"[数据库] 写入 {len(nodes_data)} 个节点耗时: {node_end - node_start:.2f}秒")
@@ -1134,7 +1130,7 @@ class EntorhinalCortex:
batch = edges_data[i : i + batch_size]
session.execute(insert(GraphEdges), batch)
session.commit()
edge_end = time.time()
logger.info(f"[数据库] 写入 {len(edges_data)} 条边耗时: {edge_end - edge_start:.2f}秒")
@@ -1170,10 +1166,7 @@ class EntorhinalCortex:
if not node.last_modified:
update_data["last_modified"] = current_time
session.execute(
update(GraphNodes).where(GraphNodes.concept == concept).values(**update_data)
)
session.execute(update(GraphNodes).where(GraphNodes.concept == concept).values(**update_data))
# 获取时间信息(如果不存在则使用当前时间)
created_time = node.created_time or current_time
@@ -1209,7 +1202,6 @@ class EntorhinalCortex:
.where((GraphEdges.source == source) & (GraphEdges.target == target))
.values(**update_data)
)
# 获取时间信息(如果不存在则使用当前时间)
created_time = edge.created_time or current_time
@@ -1231,8 +1223,10 @@ class ParahippocampalGyrus:
def __init__(self, hippocampus: Hippocampus):
self.hippocampus = hippocampus
self.memory_graph = hippocampus.memory_graph
self.memory_modify_model = LLMRequest(model_set=model_config.model_task_config.utils, request_type="memory.modify")
self.memory_modify_model = LLMRequest(
model_set=model_config.model_task_config.utils, request_type="memory.modify"
)
async def memory_compress(self, messages: list, compress_rate=0.1):
"""压缩和总结消息内容，生成记忆主题和摘要。
@@ -1532,14 +1526,20 @@ class ParahippocampalGyrus:
similarity = self._calculate_item_similarity(memory_items[i], memory_items[j])
if similarity > 0.8: # 相似度阈值
# 合并相似记忆项
longer_item = memory_items[i] if len(memory_items[i]) > len(memory_items[j]) else memory_items[j]
shorter_item = memory_items[j] if len(memory_items[i]) > len(memory_items[j]) else memory_items[i]
longer_item = (
memory_items[i] if len(memory_items[i]) > len(memory_items[j]) else memory_items[j]
)
shorter_item = (
memory_items[j] if len(memory_items[i]) > len(memory_items[j]) else memory_items[i]
)
# 保留更长的记忆项，标记短的用于删除
if shorter_item not in items_to_remove:
items_to_remove.append(shorter_item)
merged_count += 1
logger.debug(f"[整合] 在节点 {node} 中合并相似记忆: {shorter_item[:30]}... -> {longer_item[:30]}...")
logger.debug(
f"[整合] 在节点 {node} 中合并相似记忆: {shorter_item[:30]}... -> {longer_item[:30]}..."
)
# 移除被合并的记忆项
if items_to_remove:
@@ -1566,11 +1566,11 @@ class ParahippocampalGyrus:
# 检查是否有变化需要同步到数据库
has_changes = (
edge_changes["weakened"] or
edge_changes["removed"] or
node_changes["reduced"] or
node_changes["removed"] or
merged_count > 0
edge_changes["weakened"]
or edge_changes["removed"]
or node_changes["reduced"]
or node_changes["removed"]
or merged_count > 0
)
if has_changes:
@@ -1696,7 +1696,9 @@ class HippocampusManager:
response = []
return response
async def get_activate_from_text(self, text: str, max_depth: int = 3, fast_retrieval: bool = False) -> tuple[float, list[str]]:
async def get_activate_from_text(
self, text: str, max_depth: int = 3, fast_retrieval: bool = False
) -> tuple[float, list[str]]:
"""从文本中获取激活值的公共接口"""
if not self._initialized:
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
@@ -1720,6 +1722,6 @@ class HippocampusManager:
raise RuntimeError("HippocampusManager 尚未初始化，请先调用 initialize 方法")
return self._hippocampus.get_all_node_names()
# 创建全局实例
hippocampus_manager = HippocampusManager()