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better：优化工作记忆处理器

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SengokuCola
2025-06-02 20:50:05 +08:00
parent 90b73acbe5
commit 476743427a
3 changed files with 54 additions and 186 deletions
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14
src/chat/focus_chat/info_processors/working_memory_processor.py
View File

@@ -108,9 +108,7 @@ class WorkingMemoryProcessor(BaseProcessor):
memory_summary = memory.summary
memory_id = memory.id
memory_brief = memory_summary.get("brief")
# memory_detailed = memory_summary.get("detailed")
memory_keypoints = memory_summary.get("keypoints")
memory_events = memory_summary.get("events")
memory_keypoints = memory_summary.get("key_points", [])
memory_single_prompt = f"记忆id:{memory_id},记忆摘要:{memory_brief}\n"
memory_prompts.append(memory_single_prompt)
@@ -165,15 +163,9 @@ class WorkingMemoryProcessor(BaseProcessor):
memory_summary = memory.summary
memory_id = memory.id
memory_brief = memory_summary.get("brief")
# memory_detailed = memory_summary.get("detailed")
memory_keypoints = memory_summary.get("keypoints")
memory_events = memory_summary.get("events")
memory_keypoints = memory_summary.get("key_points", [])
for keypoint in memory_keypoints:
memory_str += f"记忆要点:{keypoint}\n"
for event in memory_events:
memory_str += f"记忆事件:{event}\n"
# memory_str += f"记忆摘要:{memory_detailed}\n"
# memory_str += f"记忆主题:{memory_brief}\n"
working_memory_info = WorkingMemoryInfo()
if memory_str:
@@ -225,7 +217,7 @@ class WorkingMemoryProcessor(BaseProcessor):
logger.debug(f"{self.log_prefix} 异步合并记忆成功: {memory_id1} 和 {memory_id2}...")
logger.debug(f"{self.log_prefix} 合并后的记忆梗概: {merged_memory.summary.get('brief')}")
logger.debug(f"{self.log_prefix} 合并后的记忆详情: {merged_memory.summary.get('detailed')}")
logger.debug(f"{self.log_prefix} 合并后的记忆要点: {merged_memory.summary.get('keypoints')}")
logger.debug(f"{self.log_prefix} 合并后的记忆要点: {merged_memory.summary.get('key_points')}")
logger.debug(f"{self.log_prefix} 合并后的记忆事件: {merged_memory.summary.get('events')}")
except Exception as e:

224
src/chat/focus_chat/working_memory/memory_manager.py
View File

@@ -224,39 +224,27 @@ class MemoryManager:
Returns:
包含总结、概括、关键概念和事件的字典
"""
prompt = f"""请对以下内容进行总结，总结成记忆，输出四部分：
prompt = f"""请对以下内容进行总结，总结成记忆，输出两部分：
1. 记忆内容主题（精简，20字以内）：让用户可以一眼看出记忆内容是什么
2. 记忆内容概括（200字以内）：让用户可以了解记忆内容的大致内容
3. 关键概念和知识（keypoints）：多条，提取关键的概念、知识点和关键词，要包含对概念的解释
4. 事件描述（events）：多条，描述谁（人物）在什么时候（时间）做了什么（事件）
2. key_points：多条，包含关键的概念、事件，每条都要包含解释或描述，谁在什么时候干了什么
内容：
{content}
请按以下JSON格式输出：
```json
{{
"brief": "记忆内容主题（20字以内）",
"detailed": "记忆内容概括（200字以内）",
"keypoints": [
"概念1：解释",
"概念2：解释",
...
],
"events": [
"事件1：谁在什么时候做了什么",
"事件2：谁在什么时候做了什么",
"key_points": [
"要点1：解释或描述",
"要点2：解释或描述",
...
]
}}
```
请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
"""
default_summary = {
"brief": "主题未知的记忆",
"detailed": "大致内容未知的记忆",
"keypoints": ["未知的概念"],
"events": ["未知的事件"],
"key_points": ["未知的要点"],
}
try:
@@ -288,29 +276,14 @@ class MemoryManager:
if "brief" not in json_result or not isinstance(json_result["brief"], str):
json_result["brief"] = "主题未知的记忆"
if "detailed" not in json_result or not isinstance(json_result["detailed"], str):
json_result["detailed"] = "大致内容未知的记忆"
# 处理关键概念
if "keypoints" not in json_result or not isinstance(json_result["keypoints"], list):
json_result["keypoints"] = ["未知的概念"]
# 处理关键要点
if "key_points" not in json_result or not isinstance(json_result["key_points"], list):
json_result["key_points"] = ["未知的要点"]
else:
# 确保keypoints中的每个项目都是字符串
json_result["keypoints"] = [str(point) for point in json_result["keypoints"] if point is not None]
if not json_result["keypoints"]:
json_result["keypoints"] = ["未知的概念"]
# 处理事件
if "events" not in json_result or not isinstance(json_result["events"], list):
json_result["events"] = ["未知的事件"]
else:
# 确保events中的每个项目都是字符串
json_result["events"] = [str(event) for event in json_result["events"] if event is not None]
if not json_result["events"]:
json_result["events"] = ["未知的事件"]
# 兼容旧版，将keypoints和events合并到key_points中
json_result["key_points"] = json_result["keypoints"] + json_result["events"]
# 确保key_points中的每个项目都是字符串
json_result["key_points"] = [str(point) for point in json_result["key_points"] if point is not None]
if not json_result["key_points"]:
json_result["key_points"] = ["未知的要点"]
return json_result
@@ -348,52 +321,31 @@ class MemoryManager:
# 使用LLM根据要求对总结、概括和要点进行精简修改
prompt = f"""
请根据以下要求，对记忆内容的主题、概括、关键概念和事件进行精简，模拟记忆的遗忘过程：
请根据以下要求，对记忆内容的主题和关键要点进行精简，模拟记忆的遗忘过程：
要求：{requirements}
你可以随机对关键概念和事件进行压缩，模糊或者丢弃，修改后，同样修改主题和概括
你可以随机对关键要点进行压缩，模糊或者丢弃，修改后，同样修改主题
目前主题：{summary["brief"]}
目前概括：{summary["detailed"]}
目前关键要点：
{chr(10).join([f"- {point}" for point in summary.get("key_points", [])])}
目前关键概念：
{chr(10).join([f"- {point}" for point in summary.get("keypoints", [])])}
目前事件：
{chr(10).join([f"- {point}" for point in summary.get("events", [])])}
请生成修改后的主题、概括、关键概念和事件，遵循以下格式：
请生成修改后的主题和关键要点，遵循以下格式：
```json
{{
"brief": "修改后的主题（20字以内）",
"detailed": "修改后的概括（200字以内）",
"keypoints": [
"修改后的概念1：解释",
"修改后的概念2：解释"
],
"events": [
"修改后的事件1：谁在什么时候做了什么",
"修改后的事件2：谁在什么时候做了什么"
"key_points": [
"修改后的要点1：解释或描述",
"修改后的要点2：解释或描述"
]
}}
```
请确保输出是有效的JSON格式，不要添加任何额外的说明或解释。
"""
# 检查summary中是否有旧版结构，转换为新版结构
if "keypoints" not in summary and "events" not in summary and "key_points" in summary:
# 尝试区分key_points中的keypoints和events
# 简单地将前半部分视为keypoints，后半部分视为events
key_points = summary.get("key_points", [])
halfway = len(key_points) // 2
summary["keypoints"] = key_points[:halfway] or ["未知的概念"]
summary["events"] = key_points[halfway:] or ["未知的事件"]
# 定义默认的精简结果
default_refined = {
"brief": summary["brief"],
"detailed": summary["detailed"],
"keypoints": summary.get("keypoints", ["未知的概念"])[:1], # 默认只保留第一个关键概念
"events": summary.get("events", ["未知的事件"])[:1], # 默认只保留第一个事件
"key_points": summary.get("key_points", ["未知的要点"])[:1], # 默认只保留第一个要点
}
try:
@@ -421,30 +373,17 @@ class MemoryManager:
logger.error(f"修复后的JSON不是字典类型: {type(refined_data)}")
refined_data = default_refined
# 更新总结、概括
# 更新总结
summary["brief"] = refined_data.get("brief", "主题未知的记忆")
summary["detailed"] = refined_data.get("detailed", "大致内容未知的记忆")
# 更新关键概念
keypoints = refined_data.get("keypoints", [])
if isinstance(keypoints, list) and keypoints:
# 确保所有关键概念都是字符串
summary["keypoints"] = [str(point) for point in keypoints if point is not None]
# 更新关键要点
key_points = refined_data.get("key_points", [])
if isinstance(key_points, list) and key_points:
# 确保所有要点都是字符串
summary["key_points"] = [str(point) for point in key_points if point is not None]
else:
# 如果keypoints不是列表或为空，使用默认值
summary["keypoints"] = ["主要概念已遗忘"]
# 更新事件
events = refined_data.get("events", [])
if isinstance(events, list) and events:
# 确保所有事件都是字符串
summary["events"] = [str(event) for event in events if event is not None]
else:
# 如果events不是列表或为空，使用默认值
summary["events"] = ["事件细节已遗忘"]
# 兼容旧版，维护key_points
summary["key_points"] = summary["keypoints"] + summary["events"]
# 如果key_points不是列表或为空，使用默认值
summary["key_points"] = ["主要要点已遗忘"]
except Exception as e:
logger.error(f"精简记忆出错: {str(e)}")
@@ -452,9 +391,7 @@ class MemoryManager:
# 出错时使用简化的默认精简
summary["brief"] = summary["brief"] + " (已简化)"
summary["keypoints"] = summary.get("keypoints", ["未知的概念"])[:1]
summary["events"] = summary.get("events", ["未知的事件"])[:1]
summary["key_points"] = summary["keypoints"] + summary["events"]
summary["key_points"] = summary.get("key_points", ["未知的要点"])[:1]
except Exception as e:
logger.error(f"精简记忆调用LLM出错: {str(e)}")
@@ -573,27 +510,11 @@ class MemoryManager:
# 如果有摘要信息，添加到提示中
if summary1:
prompt += f"记忆1主题：{summary1['brief']}\n"
prompt += f"记忆1概括：{summary1['detailed']}\n"
if "keypoints" in summary1:
prompt += "记忆1关键概念：\n" + "\n".join([f"- {point}" for point in summary1["keypoints"]]) + "\n\n"
if "events" in summary1:
prompt += "记忆1事件：\n" + "\n".join([f"- {point}" for point in summary1["events"]]) + "\n\n"
elif "key_points" in summary1:
prompt += "记忆1要点：\n" + "\n".join([f"- {point}" for point in summary1["key_points"]]) + "\n\n"
prompt += "记忆1关键要点：\n" + "\n".join([f"- {point}" for point in summary1.get("key_points", [])]) + "\n\n"
if summary2:
prompt += f"记忆2主题：{summary2['brief']}\n"
prompt += f"记忆2概括：{summary2['detailed']}\n"
if "keypoints" in summary2:
prompt += "记忆2关键概念：\n" + "\n".join([f"- {point}" for point in summary2["keypoints"]]) + "\n\n"
if "events" in summary2:
prompt += "记忆2事件：\n" + "\n".join([f"- {point}" for point in summary2["events"]]) + "\n\n"
elif "key_points" in summary2:
prompt += "记忆2要点：\n" + "\n".join([f"- {point}" for point in summary2["key_points"]]) + "\n\n"
prompt += "记忆2关键要点：\n" + "\n".join([f"- {point}" for point in summary2.get("key_points", [])]) + "\n\n"
# 添加记忆原始内容
prompt += f"""
@@ -608,15 +529,10 @@ class MemoryManager:
{{
"content": "合并后的记忆内容文本（尽可能保留原信息，但去除重复）",
"brief": "合并后的主题（20字以内）",
"detailed": "合并后的概括（200字以内）",
"keypoints": [
"合并后的概念1：解释",
"合并后的概念2：解释",
"合并后的概念3：解释"
],
"events": [
"合并后的事件1：谁在什么时候做了什么",
"合并后的事件2：谁在什么时候做了什么"
"key_points": [
"合并后的要点1：解释或描述",
"合并后的要点2：解释或描述",
"合并后的要点3：解释或描述"
]
}}
```
@@ -627,40 +543,18 @@ class MemoryManager:
default_merged = {
"content": f"{content1}\n\n{content2}",
"brief": f"合并：{summary1['brief']} + {summary2['brief']}",
"detailed": f"合并了两个记忆：{summary1['detailed']} 以及 {summary2['detailed']}",
"keypoints": [],
"events": [],
"key_points": [],
}
# 合并旧版key_points
# 合并key_points
if "key_points" in summary1:
default_merged["keypoints"].extend(summary1.get("keypoints", []))
default_merged["events"].extend(summary1.get("events", []))
# 如果没有新的结构，尝试从旧结构分离
if not default_merged["keypoints"] and not default_merged["events"] and "key_points" in summary1:
key_points = summary1["key_points"]
halfway = len(key_points) // 2
default_merged["keypoints"].extend(key_points[:halfway])
default_merged["events"].extend(key_points[halfway:])
default_merged["key_points"].extend(summary1["key_points"])
if "key_points" in summary2:
default_merged["keypoints"].extend(summary2.get("keypoints", []))
default_merged["events"].extend(summary2.get("events", []))
# 如果没有新的结构，尝试从旧结构分离
if not default_merged["keypoints"] and not default_merged["events"] and "key_points" in summary2:
key_points = summary2["key_points"]
halfway = len(key_points) // 2
default_merged["keypoints"].extend(key_points[:halfway])
default_merged["events"].extend(key_points[halfway:])
default_merged["key_points"].extend(summary2["key_points"])
# 确保列表不为空
if not default_merged["keypoints"]:
default_merged["keypoints"] = ["合并的关键概念"]
if not default_merged["events"]:
default_merged["events"] = ["合并的事件"]
# 添加key_points兼容
default_merged["key_points"] = default_merged["keypoints"] + default_merged["events"]
if not default_merged["key_points"]:
default_merged["key_points"] = ["合并的要点"]
try:
# 调用LLM合并记忆
@@ -694,29 +588,14 @@ class MemoryManager:
if "brief" not in merged_data or not isinstance(merged_data["brief"], str):
merged_data["brief"] = default_merged["brief"]
if "detailed" not in merged_data or not isinstance(merged_data["detailed"], str):
merged_data["detailed"] = default_merged["detailed"]
# 处理关键概念
if "keypoints" not in merged_data or not isinstance(merged_data["keypoints"], list):
merged_data["keypoints"] = default_merged["keypoints"]
# 处理关键要点
if "key_points" not in merged_data or not isinstance(merged_data["key_points"], list):
merged_data["key_points"] = default_merged["key_points"]
else:
# 确保keypoints中的每个项目都是字符串
merged_data["keypoints"] = [str(point) for point in merged_data["keypoints"] if point is not None]
if not merged_data["keypoints"]:
merged_data["keypoints"] = ["合并的关键概念"]
# 处理事件
if "events" not in merged_data or not isinstance(merged_data["events"], list):
merged_data["events"] = default_merged["events"]
else:
# 确保events中的每个项目都是字符串
merged_data["events"] = [str(event) for event in merged_data["events"] if event is not None]
if not merged_data["events"]:
merged_data["events"] = ["合并的事件"]
# 添加key_points兼容
merged_data["key_points"] = merged_data["keypoints"] + merged_data["events"]
# 确保key_points中的每个项目都是字符串
merged_data["key_points"] = [str(point) for point in merged_data["key_points"] if point is not None]
if not merged_data["key_points"]:
merged_data["key_points"] = ["合并的要点"]
except Exception as e:
logger.error(f"合并记忆时处理JSON出错: {str(e)}")
@@ -744,9 +623,6 @@ class MemoryManager:
# 设置合并后的摘要
summary = {
"brief": merged_data["brief"],
"detailed": merged_data["detailed"],
"keypoints": merged_data["keypoints"],
"events": merged_data["events"],
"key_points": merged_data["key_points"],
}
merged_memory.set_summary(summary)

2
template/bot_config_template.toml
View File

@@ -98,7 +98,7 @@ parallel_processing = true # 是否并行处理回忆和处理器阶段，可以
processor_max_time = 25 # 处理器最大时间，单位秒，如果超过这个时间，处理器会自动停止
observation_context_size = 16 # 观察到的最长上下文大小
observation_context_size = 20 # 观察到的最长上下文大小
compressed_length = 8 # 不能大于observation_context_size,心流上下文压缩的最短压缩长度，超过心流观察到的上下文长度，会压缩，最短压缩长度为5
compress_length_limit = 4 #最多压缩份数，超过该数值的压缩上下文会被删除