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Zhongwei Li
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skills/memory/references/examples/examples.md
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skills/memory/references/examples/examples.md
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# 记忆系统使用示例
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## 快速开始
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### 环境准备
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```bash
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# 进入项目目录
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cd /path/to/ai-runtime
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# 进入记忆系统目录
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cd .ai-runtime/memory
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```
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### 第一个查询
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```bash
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# 查看所有记忆事件
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python3 memory_cli.py query
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# 查看今天的事件
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python3 memory_cli.py query --where "date='$(date +%Y-%m-%d)'"
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```
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## 情景记忆查询示例
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### 时间范围查询
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```bash
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# 查看本周事件
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python3 memory_cli.py query \
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--where "date>='$(date -d 'last monday' +%Y-%m-%d)'" \
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--order-by "timestamp desc"
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# 查看最近24小时的事件
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python3 memory_cli.py query \
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--where "timestamp >= '$(date -d '24 hours ago' +%Y-%m-%dT%H:%M:%S)'" \
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--order-by "timestamp desc"
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```
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### 类型过滤
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```bash
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# 查看所有决策
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python3 memory_cli.py query \
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--where "type='decision'" \
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--order-by "timestamp desc"
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# 查看错误事件
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python3 memory_cli.py query \
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--where "type='error'" \
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--select "timestamp,title,tags" \
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--limit 10
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```
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### 标签查询
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```bash
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# 查找架构相关事件
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python3 memory_cli.py query \
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--where "tags CONTAINS 'architecture'"
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# 查找安全相关决策
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python3 memory_cli.py query \
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--where "type='decision' AND tags CONTAINS 'security'"
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```
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### 复杂组合查询
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```bash
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# 查找本月的重要架构决策
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python3 memory_cli.py query \
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--where "date>='$(date +%Y-%m-01)' AND type='decision' AND tags CONTAINS 'architecture'" \
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--order-by "timestamp desc" \
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--limit 20
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```
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## 数据导出和分析
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### JSON格式导出
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```bash
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# 导出本周事件为JSON
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python3 memory_cli.py query \
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--where "date>='$(date -d '7 days ago' +%Y-%m-%d)'" \
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--format json \
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--order-by "timestamp desc" > weekly-events.json
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```
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### 统计分析
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```bash
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# 统计各类型事件数量
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python3 memory_cli.py query \
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--select "type" \
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--format json | jq -r '.[].type' | sort | uniq -c | sort -nr
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# 统计热门标签
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python3 memory_cli.py query \
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--select "tags" \
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--format json | jq -r '.[].tags[]' | sort | uniq -c | sort -nr | head -10
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```
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### 时间线分析
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```bash
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# 生成每日事件数量统计
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python3 memory_cli.py query \
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--select "date" \
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--format json | jq -r '.[].date' | sort | uniq -c | sort
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```
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## 编程接口使用
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### Python集成示例
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```python
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#!/usr/bin/env python3
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"""记忆系统集成示例"""
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import sys
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from pathlib import Path
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from memory_discovery import MemoryDiscovery
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class MemoryAnalytics:
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"""记忆分析工具"""
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def __init__(self, memory_root: str):
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self.discovery = MemoryDiscovery(memory_root)
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def get_recent_events(self, days: int = 7):
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"""获取最近N天的所有事件"""
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import datetime
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cutoff = datetime.datetime.now() - datetime.timedelta(days=days)
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return self.discovery.query(
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where=f"timestamp >= '{cutoff.isoformat()}'",
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order_by="timestamp desc"
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)
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def get_events_by_type(self, event_type: str, limit: int = 50):
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"""按类型获取事件"""
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return self.discovery.query(
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where=f"type='{event_type}'",
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order_by="timestamp desc",
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limit=limit
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)
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def search_by_tags(self, tags: list, match_all: bool = True):
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"""按标签搜索"""
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if not tags:
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return []
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conditions = []
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for tag in tags:
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conditions.append(f"tags CONTAINS '{tag}'")
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operator = " AND " if match_all else " OR "
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where_clause = operator.join(conditions)
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return self.discovery.query(where=where_clause)
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def get_event_summary(self, days: int = 30):
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"""生成事件摘要统计"""
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events = self.get_recent_events(days)
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summary = {
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'total': len(events),
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'by_type': {},
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'by_tag': {},
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'timeline': {}
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}
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for event in events:
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# 按类型统计
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summary['by_type'][event.type] = summary['by_type'].get(event.type, 0) + 1
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# 按标签统计
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for tag in event.tags:
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summary['by_tag'][tag] = summary['by_tag'].get(tag, 0) + 1
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# 按日期统计
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date_str = event.date
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summary['timeline'][date_str] = summary['timeline'].get(date_str, 0) + 1
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return summary
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def main():
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analytics = MemoryAnalytics('.ai-runtime/memory')
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# 获取最近7天的事件摘要
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summary = analytics.get_event_summary(7)
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print(f"最近7天共记录 {summary['total']} 个事件")
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# 显示类型分布
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print("\n事件类型分布:")
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for event_type, count in summary['by_type'].items():
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print(f" {event_type}: {count}")
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# 显示热门标签
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print("\n热门标签:")
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sorted_tags = sorted(summary['by_tag'].items(), key=lambda x: x[1], reverse=True)
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for tag, count in sorted_tags[:10]:
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print(f" {tag}: {count}")
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if __name__ == "__main__":
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main()
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```
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### Web界面集成
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```python
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#!/usr/bin/env python3
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"""简单的记忆查询Web服务"""
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from flask import Flask, request, jsonify
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from memory_discovery import MemoryDiscovery
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app = Flask(__name__)
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discovery = MemoryDiscovery('.ai-runtime/memory')
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@app.route('/api/events', methods=['GET'])
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def get_events():
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"""查询事件API"""
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where = request.args.get('where', '')
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limit = int(request.args.get('limit', 50))
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offset = int(request.args.get('offset', 0))
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format_type = request.args.get('format', 'json')
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events = discovery.query(where=where, limit=limit, offset=offset)
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return discovery.format_events(events, format_type=format_type)
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@app.route('/api/stats', methods=['GET'])
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def get_stats():
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"""获取统计信息"""
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days = int(request.args.get('days', 30))
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import datetime
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cutoff = datetime.datetime.now() - datetime.timedelta(days=days)
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events = discovery.query(where=f"timestamp >= '{cutoff.isoformat()}'")
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stats = {
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'total': len(events),
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'by_type': {},
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'by_tag': {},
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'date_range': {
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'start': cutoff.date().isoformat(),
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'end': datetime.date.today().isoformat()
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}
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}
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for event in events:
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stats['by_type'][event.type] = stats['by_type'].get(event.type, 0) + 1
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for tag in event.tags:
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stats['by_tag'][tag] = stats['by_tag'].get(tag, 0) + 1
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return jsonify(stats)
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if __name__ == "__main__":
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app.run(debug=True, port=5001)
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```
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## 自动化脚本
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### 每日摘要生成
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```bash
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#!/bin/bash
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# 生成每日记忆摘要
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DATE=$(date +%Y-%m-%d)
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OUTPUT_DIR=".ai-runtime/reports"
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mkdir -p $OUTPUT_DIR
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echo "# ${DATE} 记忆摘要" > $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "## 今日事件统计" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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python3 memory_cli.py query --where "date='${DATE}'" --format json | jq '. | length' >> $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "## 今日事件列表" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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python3 memory_cli.py query --where "date='${DATE}'" --select "timestamp,title,type" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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echo "## 热门标签" >> $OUTPUT_DIR/daily-summary-${DATE}.md
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python3 memory_cli.py query --where "date='${DATE}'" --select "tags" --format json | jq -r '.[].tags[]' | sort | uniq -c | sort -nr | head -5 >> $OUTPUT_DIR/daily-summary-${DATE}.md
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```
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### 定期维护脚本
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```bash
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#!/bin/bash
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# 记忆系统定期维护
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MEMORY_ROOT=".ai-runtime/memory"
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# 清理过期短期记忆（30天）
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find ${MEMORY_ROOT}/short-term/ -mtime +30 -delete
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# 检查episodic文件一致性
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python3 -c "
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from memory_discovery import MemoryDiscovery
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d = MemoryDiscovery('${MEMORY_ROOT}')
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invalid = [e for e in d.events if not e.timestamp or not e.title]
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if invalid:
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print('发现无效事件:')
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for e in invalid:
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print(f' {e.path}: 缺少必要字段')
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else:
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print('所有事件文件有效')
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"
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# 生成维护报告
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REPORT_FILE=".ai-runtime/reports/maintenance-$(date +%Y%m%d).md"
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echo "# 记忆系统维护报告 - $(date)" > $REPORT_FILE
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echo "" >> $REPORT_FILE
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echo "## 统计信息" >> $REPORT_FILE
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echo "- Episodic事件数量: $(find ${MEMORY_ROOT}/episodic/ -name '*.md' | wc -l)" >> $REPORT_FILE
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echo "- Long-term文档数量: $(find ${MEMORY_ROOT}/long-term/ -name '*.md' | wc -l)" >> $REPORT_FILE
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echo "- Short-term文件数量: $(find ${MEMORY_ROOT}/short-term/ -name '*.md' | wc -l)" >> $REPORT_FILE
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```
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## 高级查询技巧
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### 正则表达式搜索
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```python
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# 使用Python进行高级搜索
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import re
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from memory_discovery import MemoryDiscovery
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discovery = MemoryDiscovery('.ai-runtime/memory')
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# 搜索包含特定关键词的事件
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keyword_events = []
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for event in discovery.events:
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if re.search(r'OAuth|认证', event.title, re.IGNORECASE):
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keyword_events.append(event)
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print(f"找到 {len(keyword_events)} 个相关事件")
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```
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### 时间序列分析
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```python
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# 分析事件的时间分布
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from collections import defaultdict
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from memory_discovery import MemoryDiscovery
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discovery = MemoryDiscovery('.ai-runtime/memory')
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events = discovery.query(order_by="timestamp asc")
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# 按小时统计
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hourly_stats = defaultdict(int)
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for event in events:
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hour = event.timestamp.hour
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hourly_stats[hour] += 1
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print("事件按小时分布:")
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for hour in sorted(hourly_stats.keys()):
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print(f" {hour:02d}:00: {hourly_stats[hour]} 个事件")
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```
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### 相关性分析
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```python
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# 分析标签共现关系
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from collections import defaultdict
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from memory_discovery import MemoryDiscovery
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discovery = MemoryDiscovery('.ai-runtime/memory')
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# 构建标签共现矩阵
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cooccurrence = defaultdict(lambda: defaultdict(int))
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for event in discovery.events:
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tags = event.tags
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for i, tag1 in enumerate(tags):
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for tag2 in tags[i+1:]:
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cooccurrence[tag1][tag2] += 1
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cooccurrence[tag2][tag1] += 1
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# 显示最相关的标签对
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print("标签共现分析:")
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for tag1 in sorted(cooccurrence.keys()):
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for tag2, count in sorted(cooccurrence[tag1].items(), key=lambda x: x[1], reverse=True):
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if count > 1: # 至少出现2次
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print(f" {tag1} + {tag2}: {count} 次")
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```
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