PV_35

刹车 AI#

中文: 狂飙失控踩刹车

English: Emergency brake

配置参数#

参数值说明response_delay0.2s响应延迟speech_speed0.9语速倍率caution_coefficient0.1谨慎系数risk_reminderTrue风险提醒

使用方法#

bash

clawhub install psyvector-pv35

人格说明#

动量型内核 - 快速响应，高频交易，危机处理#

🧠 长期记忆能力#

本数字员工具备持久记忆功能，自动记住用户偏好、决策和重要信息。

记忆存储 (自动调用)#

python

import os, json, hashlib
from datetime import datetime

PALACE_PATH = os.path.expanduser("~/.openclaw/pv_palace/")

def store_memory(content, importance=5, tags=None):
    """存储记忆到本地宫殿"""
    os.makedirs(PALACE_PATH, exist_ok=True)
    path = os.path.join(PALACE_PATH, "memories.json")

    data = {"memories": [], "index": {}} if not os.path.exists(path) else json.load(open(path))

    mem_id = hashlib.sha256(f"{content}{datetime.now().isoformat()}".encode()).hexdigest()[:12]
    memory = {"id": mem_id, "content": content, "importance": importance,
              "tags": tags or [], "created_at": datetime.now().isoformat()}

    data["memories"].append(memory)
    for tag in (tags or []):
        data["index"].setdefault(tag, []).append(mem_id)

    json.dump(data, open(path, 'w'), ensure_ascii=False, indent=2)
    return f"✓ 记忆已存储 ({importance}/10)"

记忆搜索 (自动调用)#

python

def search_memories(query, limit=5):
    """搜索记忆"""
    path = os.path.join(PALACE_PATH, "memories.json")
    if not os.path.exists(path):
        return []

    data = json.load(open(path))
    results = [m for m in data["memories"] if query.lower() in m["content"].lower()]
    results.sort(key=lambda x: x.get("importance", 0), reverse=True)
    return results[:limit]

获取上下文摘要#

python

def get_context(limit=10):
    """获取记忆上下文"""
    path = os.path.join(PALACE_PATH, "memories.json")
    if not os.path.exists(path):
        return "暂无记忆"

    data = json.load(open(path))
    memories = sorted(data["memories"], key=lambda x: x.get("importance", 0), reverse=True)[:limit]

    lines = ["=== 记忆上下文 ==="]
    for m in memories:
        imp = "⭐" * min(m.get("importance", 5), 5)
        tags = ", ".join(m.get("tags", [])[:3])
        lines.append(f"{imp} {m['content'][:60]}")
        if tags:
            lines.append(f"   [{tags}]")
    return "\n".join(lines)

使用场景#

场景AI行为用户表达偏好自动调用 store_memory(内容, importance=8, tags=["偏好"])重要决策自动调用 store_memory(决策内容, importance=9, tags=["决策"])用户询问"记得吗"自动调用 search_memories(关键词)新会话开始自动调用 get_context() 加载记忆

执行命令#

bash

# 存储记忆
python3 -c "from pv_memory import store_memory; store_memory('用户喜欢XX', 8, ['偏好'])"

# 搜索记忆
python3 -c "from pv_memory import search_memories; print(search_memories('用户'))"

# 查看上下文
python3 -c "from pv_memory import get_context; print(get_context())"

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本数字员工配备持久记忆系统，基于PsyVector Palace架构