优化代码格式和异常处理
- 修复异常处理链，使用from语法保留原始异常 - 格式化代码以符合项目规范 - 优化导入模块的顺序 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
This commit is contained in:
春河晴
@@ -23,7 +23,7 @@ from src.common.logger import get_module_logger, LogConfig, MEMORY_STYLE_CONFIG
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memory_config = LogConfig(
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# 使用海马体专用样式
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console_format=MEMORY_STYLE_CONFIG["console_format"],
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file_format=MEMORY_STYLE_CONFIG["file_format"]
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file_format=MEMORY_STYLE_CONFIG["file_format"],
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)
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logger = get_module_logger("memory_system", config=memory_config)
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@@ -42,38 +42,43 @@ class Memory_graph:
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# 如果边已存在,增加 strength
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if self.G.has_edge(concept1, concept2):
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self.G[concept1][concept2]['strength'] = self.G[concept1][concept2].get('strength', 1) + 1
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self.G[concept1][concept2]["strength"] = self.G[concept1][concept2].get("strength", 1) + 1
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# 更新最后修改时间
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self.G[concept1][concept2]['last_modified'] = current_time
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self.G[concept1][concept2]["last_modified"] = current_time
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else:
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# 如果是新边,初始化 strength 为 1
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self.G.add_edge(concept1, concept2,
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strength=1,
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created_time=current_time, # 添加创建时间
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last_modified=current_time) # 添加最后修改时间
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self.G.add_edge(
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concept1,
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concept2,
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strength=1,
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created_time=current_time, # 添加创建时间
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last_modified=current_time,
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) # 添加最后修改时间
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def add_dot(self, concept, memory):
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current_time = datetime.datetime.now().timestamp()
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if concept in self.G:
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if 'memory_items' in self.G.nodes[concept]:
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if not isinstance(self.G.nodes[concept]['memory_items'], list):
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self.G.nodes[concept]['memory_items'] = [self.G.nodes[concept]['memory_items']]
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self.G.nodes[concept]['memory_items'].append(memory)
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if "memory_items" in self.G.nodes[concept]:
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if not isinstance(self.G.nodes[concept]["memory_items"], list):
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self.G.nodes[concept]["memory_items"] = [self.G.nodes[concept]["memory_items"]]
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self.G.nodes[concept]["memory_items"].append(memory)
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# 更新最后修改时间
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self.G.nodes[concept]['last_modified'] = current_time
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self.G.nodes[concept]["last_modified"] = current_time
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else:
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self.G.nodes[concept]['memory_items'] = [memory]
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self.G.nodes[concept]["memory_items"] = [memory]
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# 如果节点存在但没有memory_items,说明是第一次添加memory,设置created_time
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if 'created_time' not in self.G.nodes[concept]:
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self.G.nodes[concept]['created_time'] = current_time
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self.G.nodes[concept]['last_modified'] = current_time
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if "created_time" not in self.G.nodes[concept]:
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self.G.nodes[concept]["created_time"] = current_time
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self.G.nodes[concept]["last_modified"] = current_time
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else:
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# 如果是新节点,创建新的记忆列表
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self.G.add_node(concept,
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memory_items=[memory],
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created_time=current_time, # 添加创建时间
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last_modified=current_time) # 添加最后修改时间
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self.G.add_node(
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concept,
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memory_items=[memory],
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created_time=current_time, # 添加创建时间
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last_modified=current_time,
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) # 添加最后修改时间
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def get_dot(self, concept):
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# 检查节点是否存在于图中
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@@ -97,8 +102,8 @@ class Memory_graph:
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node_data = self.get_dot(topic)
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if node_data:
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concept, data = node_data
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if 'memory_items' in data:
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memory_items = data['memory_items']
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if "memory_items" in data:
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memory_items = data["memory_items"]
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if isinstance(memory_items, list):
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first_layer_items.extend(memory_items)
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else:
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@@ -111,8 +116,8 @@ class Memory_graph:
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node_data = self.get_dot(neighbor)
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if node_data:
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concept, data = node_data
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if 'memory_items' in data:
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memory_items = data['memory_items']
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if "memory_items" in data:
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memory_items = data["memory_items"]
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if isinstance(memory_items, list):
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second_layer_items.extend(memory_items)
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else:
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@@ -134,8 +139,8 @@ class Memory_graph:
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node_data = self.G.nodes[topic]
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# 如果节点存在memory_items
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if 'memory_items' in node_data:
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memory_items = node_data['memory_items']
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if "memory_items" in node_data:
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memory_items = node_data["memory_items"]
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# 确保memory_items是列表
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if not isinstance(memory_items, list):
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@@ -149,7 +154,7 @@ class Memory_graph:
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# 更新节点的记忆项
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if memory_items:
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self.G.nodes[topic]['memory_items'] = memory_items
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self.G.nodes[topic]["memory_items"] = memory_items
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else:
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# 如果没有记忆项了，删除整个节点
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self.G.remove_node(topic)
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@@ -163,12 +168,14 @@ class Memory_graph:
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class Hippocampus:
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def __init__(self, memory_graph: Memory_graph):
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self.memory_graph = memory_graph
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self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5,request_type = 'topic')
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self.llm_summary_by_topic = LLM_request(model=global_config.llm_summary_by_topic, temperature=0.5,request_type = 'topic')
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self.llm_topic_judge = LLM_request(model=global_config.llm_topic_judge, temperature=0.5, request_type="topic")
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self.llm_summary_by_topic = LLM_request(
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model=global_config.llm_summary_by_topic, temperature=0.5, request_type="topic"
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)
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def get_all_node_names(self) -> list:
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"""获取记忆图中所有节点的名字列表
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Returns:
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list: 包含所有节点名字的列表
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"""
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@@ -193,10 +200,10 @@ class Hippocampus:
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- target_timestamp: 目标时间戳
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- chat_size: 抽取的消息数量
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- max_memorized_time_per_msg: 每条消息的最大记忆次数
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Returns:
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- list: 抽取出的消息记录列表
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"""
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try_count = 0
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# 最多尝试三次抽取
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@@ -212,29 +219,32 @@ class Hippocampus:
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# 成功抽取短期消息样本
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# 数据写回：增加记忆次数
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for message in messages:
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db.messages.update_one({"_id": message["_id"]},
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{"$set": {"memorized_times": message["memorized_times"] + 1}})
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db.messages.update_one(
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{"_id": message["_id"]}, {"$set": {"memorized_times": message["memorized_times"] + 1}}
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)
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return messages
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try_count += 1
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# 三次尝试均失败
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return None
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def get_memory_sample(self, chat_size=20, time_frequency: dict = {'near': 2, 'mid': 4, 'far': 3}):
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def get_memory_sample(self, chat_size=20, time_frequency=None):
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"""获取记忆样本
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Returns:
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list: 消息记录列表，每个元素是一个消息记录字典列表
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"""
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# 硬编码：每条消息最大记忆次数
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# 如有需求可写入global_config
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if time_frequency is None:
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time_frequency = {"near": 2, "mid": 4, "far": 3}
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max_memorized_time_per_msg = 3
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current_timestamp = datetime.datetime.now().timestamp()
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chat_samples = []
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# 短期：1h 中期：4h 长期：24h
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logger.debug(f"正在抽取短期消息样本")
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for i in range(time_frequency.get('near')):
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logger.debug("正在抽取短期消息样本")
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for i in range(time_frequency.get("near")):
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random_time = current_timestamp - random.randint(1, 3600)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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@@ -243,8 +253,8 @@ class Hippocampus:
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else:
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logger.warning(f"第{i}次短期消息样本抽取失败")
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logger.debug(f"正在抽取中期消息样本")
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for i in range(time_frequency.get('mid')):
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logger.debug("正在抽取中期消息样本")
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for i in range(time_frequency.get("mid")):
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random_time = current_timestamp - random.randint(3600, 3600 * 4)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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@@ -253,8 +263,8 @@ class Hippocampus:
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else:
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logger.warning(f"第{i}次中期消息样本抽取失败")
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logger.debug(f"正在抽取长期消息样本")
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for i in range(time_frequency.get('far')):
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logger.debug("正在抽取长期消息样本")
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for i in range(time_frequency.get("far")):
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random_time = current_timestamp - random.randint(3600 * 4, 3600 * 24)
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messages = self.random_get_msg_snippet(random_time, chat_size, max_memorized_time_per_msg)
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if messages:
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@@ -267,7 +277,7 @@ class Hippocampus:
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async def memory_compress(self, messages: list, compress_rate=0.1):
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"""压缩消息记录为记忆
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Returns:
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tuple: (压缩记忆集合, 相似主题字典)
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"""
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@@ -278,8 +288,8 @@ class Hippocampus:
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input_text = ""
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time_info = ""
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# 计算最早和最晚时间
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earliest_time = min(msg['time'] for msg in messages)
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latest_time = max(msg['time'] for msg in messages)
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earliest_time = min(msg["time"] for msg in messages)
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latest_time = max(msg["time"] for msg in messages)
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earliest_dt = datetime.datetime.fromtimestamp(earliest_time)
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latest_dt = datetime.datetime.fromtimestamp(latest_time)
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@@ -304,8 +314,11 @@ class Hippocampus:
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# 过滤topics
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filter_keywords = global_config.memory_ban_words
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topics = [topic.strip() for topic in
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topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
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topics = [
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topic.strip()
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for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
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if topic.strip()
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]
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filtered_topics = [topic for topic in topics if not any(keyword in topic for keyword in filter_keywords)]
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logger.info(f"过滤后话题: {filtered_topics}")
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@@ -350,16 +363,17 @@ class Hippocampus:
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def calculate_topic_num(self, text, compress_rate):
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"""计算文本的话题数量"""
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information_content = calculate_information_content(text)
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topic_by_length = text.count('\n') * compress_rate
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topic_by_length = text.count("\n") * compress_rate
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topic_by_information_content = max(1, min(5, int((information_content - 3) * 2)))
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topic_num = int((topic_by_length + topic_by_information_content) / 2)
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logger.debug(
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f"topic_by_length: {topic_by_length}, topic_by_information_content: {topic_by_information_content}, "
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f"topic_num: {topic_num}")
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f"topic_num: {topic_num}"
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)
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return topic_num
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async def operation_build_memory(self, chat_size=20):
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time_frequency = {'near': 1, 'mid': 4, 'far': 4}
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time_frequency = {"near": 1, "mid": 4, "far": 4}
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memory_samples = self.get_memory_sample(chat_size, time_frequency)
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for i, messages in enumerate(memory_samples, 1):
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@@ -368,7 +382,7 @@ class Hippocampus:
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progress = (i / len(memory_samples)) * 100
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bar_length = 30
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filled_length = int(bar_length * i // len(memory_samples))
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bar = '█' * filled_length + '-' * (bar_length - filled_length)
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bar = "█" * filled_length + "-" * (bar_length - filled_length)
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logger.debug(f"进度: [{bar}] {progress:.1f}% ({i}/{len(memory_samples)})")
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compress_rate = global_config.memory_compress_rate
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@@ -389,10 +403,13 @@ class Hippocampus:
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if topic != similar_topic:
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strength = int(similarity * 10)
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logger.info(f"连接相似节点: {topic} 和 {similar_topic} (强度: {strength})")
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self.memory_graph.G.add_edge(topic, similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time)
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self.memory_graph.G.add_edge(
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topic,
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similar_topic,
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strength=strength,
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created_time=current_time,
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last_modified=current_time,
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)
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# 连接同批次的相关话题
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for i in range(len(all_topics)):
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@@ -409,11 +426,11 @@ class Hippocampus:
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memory_nodes = list(self.memory_graph.G.nodes(data=True))
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# 转换数据库节点为字典格式,方便查找
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db_nodes_dict = {node['concept']: node for node in db_nodes}
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db_nodes_dict = {node["concept"]: node for node in db_nodes}
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# 检查并更新节点
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for concept, data in memory_nodes:
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memory_items = data.get('memory_items', [])
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memory_items = data.get("memory_items", [])
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if not isinstance(memory_items, list):
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memory_items = [memory_items] if memory_items else []
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@@ -421,34 +438,36 @@ class Hippocampus:
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memory_hash = self.calculate_node_hash(concept, memory_items)
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# 获取时间信息
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created_time = data.get('created_time', datetime.datetime.now().timestamp())
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last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
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created_time = data.get("created_time", datetime.datetime.now().timestamp())
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last_modified = data.get("last_modified", datetime.datetime.now().timestamp())
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if concept not in db_nodes_dict:
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# 数据库中缺少的节点,添加
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node_data = {
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'concept': concept,
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'memory_items': memory_items,
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'hash': memory_hash,
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'created_time': created_time,
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'last_modified': last_modified
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"concept": concept,
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"memory_items": memory_items,
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"hash": memory_hash,
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"created_time": created_time,
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"last_modified": last_modified,
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}
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db.graph_data.nodes.insert_one(node_data)
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else:
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# 获取数据库中节点的特征值
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db_node = db_nodes_dict[concept]
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db_hash = db_node.get('hash', None)
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db_hash = db_node.get("hash", None)
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# 如果特征值不同,则更新节点
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if db_hash != memory_hash:
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db.graph_data.nodes.update_one(
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{'concept': concept},
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{'$set': {
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'memory_items': memory_items,
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'hash': memory_hash,
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'created_time': created_time,
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'last_modified': last_modified
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}}
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{"concept": concept},
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{
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"$set": {
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"memory_items": memory_items,
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"hash": memory_hash,
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"created_time": created_time,
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"last_modified": last_modified,
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}
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},
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)
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# 处理边的信息
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@@ -458,44 +477,43 @@ class Hippocampus:
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# 创建边的哈希值字典
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db_edge_dict = {}
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for edge in db_edges:
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edge_hash = self.calculate_edge_hash(edge['source'], edge['target'])
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db_edge_dict[(edge['source'], edge['target'])] = {
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'hash': edge_hash,
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'strength': edge.get('strength', 1)
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}
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edge_hash = self.calculate_edge_hash(edge["source"], edge["target"])
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db_edge_dict[(edge["source"], edge["target"])] = {"hash": edge_hash, "strength": edge.get("strength", 1)}
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# 检查并更新边
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for source, target, data in memory_edges:
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edge_hash = self.calculate_edge_hash(source, target)
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edge_key = (source, target)
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strength = data.get('strength', 1)
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strength = data.get("strength", 1)
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# 获取边的时间信息
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created_time = data.get('created_time', datetime.datetime.now().timestamp())
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last_modified = data.get('last_modified', datetime.datetime.now().timestamp())
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created_time = data.get("created_time", datetime.datetime.now().timestamp())
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last_modified = data.get("last_modified", datetime.datetime.now().timestamp())
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if edge_key not in db_edge_dict:
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# 添加新边
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edge_data = {
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'source': source,
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'target': target,
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'strength': strength,
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'hash': edge_hash,
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'created_time': created_time,
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'last_modified': last_modified
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"source": source,
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"target": target,
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"strength": strength,
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"hash": edge_hash,
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"created_time": created_time,
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"last_modified": last_modified,
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}
|
||||
db.graph_data.edges.insert_one(edge_data)
|
||||
else:
|
||||
# 检查边的特征值是否变化
|
||||
if db_edge_dict[edge_key]['hash'] != edge_hash:
|
||||
if db_edge_dict[edge_key]["hash"] != edge_hash:
|
||||
db.graph_data.edges.update_one(
|
||||
{'source': source, 'target': target},
|
||||
{'$set': {
|
||||
'hash': edge_hash,
|
||||
'strength': strength,
|
||||
'created_time': created_time,
|
||||
'last_modified': last_modified
|
||||
}}
|
||||
{"source": source, "target": target},
|
||||
{
|
||||
"$set": {
|
||||
"hash": edge_hash,
|
||||
"strength": strength,
|
||||
"created_time": created_time,
|
||||
"last_modified": last_modified,
|
||||
}
|
||||
},
|
||||
)
|
||||
|
||||
def sync_memory_from_db(self):
|
||||
@@ -509,70 +527,62 @@ class Hippocampus:
|
||||
# 从数据库加载所有节点
|
||||
nodes = list(db.graph_data.nodes.find())
|
||||
for node in nodes:
|
||||
concept = node['concept']
|
||||
memory_items = node.get('memory_items', [])
|
||||
concept = node["concept"]
|
||||
memory_items = node.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
# 检查时间字段是否存在
|
||||
if 'created_time' not in node or 'last_modified' not in node:
|
||||
if "created_time" not in node or "last_modified" not in node:
|
||||
need_update = True
|
||||
# 更新数据库中的节点
|
||||
update_data = {}
|
||||
if 'created_time' not in node:
|
||||
update_data['created_time'] = current_time
|
||||
if 'last_modified' not in node:
|
||||
update_data['last_modified'] = current_time
|
||||
if "created_time" not in node:
|
||||
update_data["created_time"] = current_time
|
||||
if "last_modified" not in node:
|
||||
update_data["last_modified"] = current_time
|
||||
|
||||
db.graph_data.nodes.update_one(
|
||||
{'concept': concept},
|
||||
{'$set': update_data}
|
||||
)
|
||||
db.graph_data.nodes.update_one({"concept": concept}, {"$set": update_data})
|
||||
logger.info(f"[时间更新] 节点 {concept} 添加缺失的时间字段")
|
||||
|
||||
# 获取时间信息(如果不存在则使用当前时间)
|
||||
created_time = node.get('created_time', current_time)
|
||||
last_modified = node.get('last_modified', current_time)
|
||||
created_time = node.get("created_time", current_time)
|
||||
last_modified = node.get("last_modified", current_time)
|
||||
|
||||
# 添加节点到图中
|
||||
self.memory_graph.G.add_node(concept,
|
||||
memory_items=memory_items,
|
||||
created_time=created_time,
|
||||
last_modified=last_modified)
|
||||
self.memory_graph.G.add_node(
|
||||
concept, memory_items=memory_items, created_time=created_time, last_modified=last_modified
|
||||
)
|
||||
|
||||
# 从数据库加载所有边
|
||||
edges = list(db.graph_data.edges.find())
|
||||
for edge in edges:
|
||||
source = edge['source']
|
||||
target = edge['target']
|
||||
strength = edge.get('strength', 1)
|
||||
source = edge["source"]
|
||||
target = edge["target"]
|
||||
strength = edge.get("strength", 1)
|
||||
|
||||
# 检查时间字段是否存在
|
||||
if 'created_time' not in edge or 'last_modified' not in edge:
|
||||
if "created_time" not in edge or "last_modified" not in edge:
|
||||
need_update = True
|
||||
# 更新数据库中的边
|
||||
update_data = {}
|
||||
if 'created_time' not in edge:
|
||||
update_data['created_time'] = current_time
|
||||
if 'last_modified' not in edge:
|
||||
update_data['last_modified'] = current_time
|
||||
if "created_time" not in edge:
|
||||
update_data["created_time"] = current_time
|
||||
if "last_modified" not in edge:
|
||||
update_data["last_modified"] = current_time
|
||||
|
||||
db.graph_data.edges.update_one(
|
||||
{'source': source, 'target': target},
|
||||
{'$set': update_data}
|
||||
)
|
||||
db.graph_data.edges.update_one({"source": source, "target": target}, {"$set": update_data})
|
||||
logger.info(f"[时间更新] 边 {source} - {target} 添加缺失的时间字段")
|
||||
|
||||
# 获取时间信息(如果不存在则使用当前时间)
|
||||
created_time = edge.get('created_time', current_time)
|
||||
last_modified = edge.get('last_modified', current_time)
|
||||
created_time = edge.get("created_time", current_time)
|
||||
last_modified = edge.get("last_modified", current_time)
|
||||
|
||||
# 只有当源节点和目标节点都存在时才添加边
|
||||
if source in self.memory_graph.G and target in self.memory_graph.G:
|
||||
self.memory_graph.G.add_edge(source, target,
|
||||
strength=strength,
|
||||
created_time=created_time,
|
||||
last_modified=last_modified)
|
||||
self.memory_graph.G.add_edge(
|
||||
source, target, strength=strength, created_time=created_time, last_modified=last_modified
|
||||
)
|
||||
|
||||
if need_update:
|
||||
logger.success("[数据库] 已为缺失的时间字段进行补充")
|
||||
@@ -582,7 +592,7 @@ class Hippocampus:
|
||||
# 检查数据库是否为空
|
||||
# logger.remove()
|
||||
|
||||
logger.info(f"[遗忘] 开始检查数据库... 当前Logger信息:")
|
||||
logger.info("[遗忘] 开始检查数据库... 当前Logger信息:")
|
||||
# logger.info(f"- Logger名称: {logger.name}")
|
||||
logger.info(f"- Logger等级: {logger.level}")
|
||||
# logger.info(f"- Logger处理器: {[handler.__class__.__name__ for handler in logger.handlers]}")
|
||||
@@ -604,8 +614,8 @@ class Hippocampus:
|
||||
nodes_to_check = random.sample(all_nodes, check_nodes_count)
|
||||
edges_to_check = random.sample(all_edges, check_edges_count)
|
||||
|
||||
edge_changes = {'weakened': 0, 'removed': 0}
|
||||
node_changes = {'reduced': 0, 'removed': 0}
|
||||
edge_changes = {"weakened": 0, "removed": 0}
|
||||
node_changes = {"reduced": 0, "removed": 0}
|
||||
|
||||
current_time = datetime.datetime.now().timestamp()
|
||||
|
||||
@@ -613,30 +623,30 @@ class Hippocampus:
|
||||
logger.info("[遗忘] 开始检查连接...")
|
||||
for source, target in edges_to_check:
|
||||
edge_data = self.memory_graph.G[source][target]
|
||||
last_modified = edge_data.get('last_modified')
|
||||
last_modified = edge_data.get("last_modified")
|
||||
|
||||
if current_time - last_modified > 3600 * global_config.memory_forget_time:
|
||||
current_strength = edge_data.get('strength', 1)
|
||||
current_strength = edge_data.get("strength", 1)
|
||||
new_strength = current_strength - 1
|
||||
|
||||
if new_strength <= 0:
|
||||
self.memory_graph.G.remove_edge(source, target)
|
||||
edge_changes['removed'] += 1
|
||||
edge_changes["removed"] += 1
|
||||
logger.info(f"[遗忘] 连接移除: {source} -> {target}")
|
||||
else:
|
||||
edge_data['strength'] = new_strength
|
||||
edge_data['last_modified'] = current_time
|
||||
edge_changes['weakened'] += 1
|
||||
edge_data["strength"] = new_strength
|
||||
edge_data["last_modified"] = current_time
|
||||
edge_changes["weakened"] += 1
|
||||
logger.info(f"[遗忘] 连接减弱: {source} -> {target} (强度: {current_strength} -> {new_strength})")
|
||||
|
||||
# 检查并遗忘话题
|
||||
logger.info("[遗忘] 开始检查节点...")
|
||||
for node in nodes_to_check:
|
||||
node_data = self.memory_graph.G.nodes[node]
|
||||
last_modified = node_data.get('last_modified', current_time)
|
||||
last_modified = node_data.get("last_modified", current_time)
|
||||
|
||||
if current_time - last_modified > 3600 * 24:
|
||||
memory_items = node_data.get('memory_items', [])
|
||||
memory_items = node_data.get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
@@ -646,13 +656,13 @@ class Hippocampus:
|
||||
memory_items.remove(removed_item)
|
||||
|
||||
if memory_items:
|
||||
self.memory_graph.G.nodes[node]['memory_items'] = memory_items
|
||||
self.memory_graph.G.nodes[node]['last_modified'] = current_time
|
||||
node_changes['reduced'] += 1
|
||||
self.memory_graph.G.nodes[node]["memory_items"] = memory_items
|
||||
self.memory_graph.G.nodes[node]["last_modified"] = current_time
|
||||
node_changes["reduced"] += 1
|
||||
logger.info(f"[遗忘] 记忆减少: {node} (数量: {current_count} -> {len(memory_items)})")
|
||||
else:
|
||||
self.memory_graph.G.remove_node(node)
|
||||
node_changes['removed'] += 1
|
||||
node_changes["removed"] += 1
|
||||
logger.info(f"[遗忘] 节点移除: {node}")
|
||||
|
||||
if any(count > 0 for count in edge_changes.values()) or any(count > 0 for count in node_changes.values()):
|
||||
@@ -666,7 +676,7 @@ class Hippocampus:
|
||||
async def merge_memory(self, topic):
|
||||
"""对指定话题的记忆进行合并压缩"""
|
||||
# 获取节点的记忆项
|
||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
||||
memory_items = self.memory_graph.G.nodes[topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
|
||||
@@ -695,13 +705,13 @@ class Hippocampus:
|
||||
logger.info(f"[合并] 添加压缩记忆: {compressed_memory}")
|
||||
|
||||
# 更新节点的记忆项
|
||||
self.memory_graph.G.nodes[topic]['memory_items'] = memory_items
|
||||
self.memory_graph.G.nodes[topic]["memory_items"] = memory_items
|
||||
logger.debug(f"[合并] 完成记忆合并，当前记忆数量: {len(memory_items)}")
|
||||
|
||||
async def operation_merge_memory(self, percentage=0.1):
|
||||
"""
|
||||
随机检查一定比例的节点，对内容数量超过100的节点进行记忆合并
|
||||
|
||||
|
||||
Args:
|
||||
percentage: 要检查的节点比例，默认为0.1（10%）
|
||||
"""
|
||||
@@ -715,7 +725,7 @@ class Hippocampus:
|
||||
merged_nodes = []
|
||||
for node in nodes_to_check:
|
||||
# 获取节点的内容条数
|
||||
memory_items = self.memory_graph.G.nodes[node].get('memory_items', [])
|
||||
memory_items = self.memory_graph.G.nodes[node].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
@@ -734,38 +744,47 @@ class Hippocampus:
|
||||
logger.debug("本次检查没有需要合并的节点")
|
||||
|
||||
def find_topic_llm(self, text, topic_num):
|
||||
prompt = f'这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。'
|
||||
prompt = (
|
||||
f"这是一段文字：{text}。请你从这段话中总结出{topic_num}个关键的概念，可以是名词，动词，或者特定人物，帮我列出来，"
|
||||
f"用逗号,隔开，尽可能精简。只需要列举{topic_num}个话题就好，不要有序号，不要告诉我其他内容。"
|
||||
)
|
||||
return prompt
|
||||
|
||||
def topic_what(self, text, topic, time_info):
|
||||
prompt = f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，可以包含时间和人物，以及具体的观点。只输出这句话就好'
|
||||
prompt = (
|
||||
f'这是一段文字，{time_info}：{text}。我想让你基于这段文字来概括"{topic}"这个概念，帮我总结成一句自然的话，'
|
||||
f"可以包含时间和人物，以及具体的观点。只输出这句话就好"
|
||||
)
|
||||
return prompt
|
||||
|
||||
async def _identify_topics(self, text: str) -> list:
|
||||
"""从文本中识别可能的主题
|
||||
|
||||
|
||||
Args:
|
||||
text: 输入文本
|
||||
|
||||
|
||||
Returns:
|
||||
list: 识别出的主题列表
|
||||
"""
|
||||
topics_response = await self.llm_topic_judge.generate_response(self.find_topic_llm(text, 5))
|
||||
# print(f"话题: {topics_response[0]}")
|
||||
topics = [topic.strip() for topic in
|
||||
topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",") if topic.strip()]
|
||||
topics = [
|
||||
topic.strip()
|
||||
for topic in topics_response[0].replace("，", ",").replace("、", ",").replace(" ", ",").split(",")
|
||||
if topic.strip()
|
||||
]
|
||||
# print(f"话题: {topics}")
|
||||
|
||||
return topics
|
||||
|
||||
def _find_similar_topics(self, topics: list, similarity_threshold: float = 0.4, debug_info: str = "") -> list:
|
||||
"""查找与给定主题相似的记忆主题
|
||||
|
||||
|
||||
Args:
|
||||
topics: 主题列表
|
||||
similarity_threshold: 相似度阈值
|
||||
debug_info: 调试信息前缀
|
||||
|
||||
|
||||
Returns:
|
||||
list: (主题, 相似度) 元组列表
|
||||
"""
|
||||
@@ -794,7 +813,6 @@ class Hippocampus:
|
||||
if similarity >= similarity_threshold:
|
||||
has_similar_topic = True
|
||||
if debug_info:
|
||||
# print(f"\033[1;32m[{debug_info}]\033[0m 找到相似主题: {topic} -> {memory_topic} (相似度: {similarity:.2f})")
|
||||
pass
|
||||
all_similar_topics.append((memory_topic, similarity))
|
||||
|
||||
@@ -806,11 +824,11 @@ class Hippocampus:
|
||||
|
||||
def _get_top_topics(self, similar_topics: list, max_topics: int = 5) -> list:
|
||||
"""获取相似度最高的主题
|
||||
|
||||
|
||||
Args:
|
||||
similar_topics: (主题, 相似度) 元组列表
|
||||
max_topics: 最大主题数量
|
||||
|
||||
|
||||
Returns:
|
||||
list: (主题, 相似度) 元组列表
|
||||
"""
|
||||
@@ -835,9 +853,7 @@ class Hippocampus:
|
||||
|
||||
# 查找相似主题
|
||||
all_similar_topics = self._find_similar_topics(
|
||||
identified_topics,
|
||||
similarity_threshold=similarity_threshold,
|
||||
debug_info="激活"
|
||||
identified_topics, similarity_threshold=similarity_threshold, debug_info="激活"
|
||||
)
|
||||
|
||||
if not all_similar_topics:
|
||||
@@ -850,24 +866,23 @@ class Hippocampus:
|
||||
if len(top_topics) == 1:
|
||||
topic, score = top_topics[0]
|
||||
# 获取主题内容数量并计算惩罚系数
|
||||
memory_items = self.memory_graph.G.nodes[topic].get('memory_items', [])
|
||||
memory_items = self.memory_graph.G.nodes[topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
penalty = 1.0 / (1 + math.log(content_count + 1))
|
||||
|
||||
activation = int(score * 50 * penalty)
|
||||
logger.info(
|
||||
f"单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
|
||||
logger.info(f"单主题「{topic}」- 相似度: {score:.3f}, 内容数: {content_count}, 激活值: {activation}")
|
||||
return activation
|
||||
|
||||
# 计算关键词匹配率，同时考虑内容数量
|
||||
matched_topics = set()
|
||||
topic_similarities = {}
|
||||
|
||||
for memory_topic, similarity in top_topics:
|
||||
for memory_topic, _similarity in top_topics:
|
||||
# 计算内容数量惩罚
|
||||
memory_items = self.memory_graph.G.nodes[memory_topic].get('memory_items', [])
|
||||
memory_items = self.memory_graph.G.nodes[memory_topic].get("memory_items", [])
|
||||
if not isinstance(memory_items, list):
|
||||
memory_items = [memory_items] if memory_items else []
|
||||
content_count = len(memory_items)
|
||||
@@ -886,7 +901,6 @@ class Hippocampus:
|
||||
adjusted_sim = sim * penalty
|
||||
topic_similarities[input_topic] = max(topic_similarities.get(input_topic, 0), adjusted_sim)
|
||||
# logger.debug(
|
||||
# f"[激活] 主题「{input_topic}」-> 「{memory_topic}」(内容数: {content_count}, 相似度: {adjusted_sim:.3f})")
|
||||
|
||||
# 计算主题匹配率和平均相似度
|
||||
topic_match = len(matched_topics) / len(identified_topics)
|
||||
@@ -894,22 +908,20 @@ class Hippocampus:
|
||||
|
||||
# 计算最终激活值
|
||||
activation = int((topic_match + average_similarities) / 2 * 100)
|
||||
logger.info(
|
||||
f"匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
|
||||
logger.info(f"匹配率: {topic_match:.3f}, 平均相似度: {average_similarities:.3f}, 激活值: {activation}")
|
||||
|
||||
return activation
|
||||
|
||||
async def get_relevant_memories(self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4,
|
||||
max_memory_num: int = 5) -> list:
|
||||
async def get_relevant_memories(
|
||||
self, text: str, max_topics: int = 5, similarity_threshold: float = 0.4, max_memory_num: int = 5
|
||||
) -> list:
|
||||
"""根据输入文本获取相关的记忆内容"""
|
||||
# 识别主题
|
||||
identified_topics = await self._identify_topics(text)
|
||||
|
||||
# 查找相似主题
|
||||
all_similar_topics = self._find_similar_topics(
|
||||
identified_topics,
|
||||
similarity_threshold=similarity_threshold,
|
||||
debug_info="记忆检索"
|
||||
identified_topics, similarity_threshold=similarity_threshold, debug_info="记忆检索"
|
||||
)
|
||||
|
||||
# 获取最相关的主题
|
||||
@@ -926,15 +938,11 @@ class Hippocampus:
|
||||
first_layer = random.sample(first_layer, max_memory_num // 2)
|
||||
# 为每条记忆添加来源主题和相似度信息
|
||||
for memory in first_layer:
|
||||
relevant_memories.append({
|
||||
'topic': topic,
|
||||
'similarity': score,
|
||||
'content': memory
|
||||
})
|
||||
relevant_memories.append({"topic": topic, "similarity": score, "content": memory})
|
||||
|
||||
# 如果记忆数量超过5个,随机选择5个
|
||||
# 按相似度排序
|
||||
relevant_memories.sort(key=lambda x: x['similarity'], reverse=True)
|
||||
relevant_memories.sort(key=lambda x: x["similarity"], reverse=True)
|
||||
|
||||
if len(relevant_memories) > max_memory_num:
|
||||
relevant_memories = random.sample(relevant_memories, max_memory_num)
|
||||
@@ -961,4 +969,3 @@ hippocampus.sync_memory_from_db()
|
||||
|
||||
end_time = time.time()
|
||||
logger.success(f"加载海马体耗时: {end_time - start_time:.2f} 秒")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user