Backup before system reinstall
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llama-research
39
jixia_academy/core/memory_bank/base_memory_bank.py
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39
jixia_academy/core/memory_bank/base_memory_bank.py
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@@ -0,0 +1,39 @@
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#!/usr/bin/env python3
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"""
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通用记忆银行抽象，便于插入不同后端（Vertex、Cloudflare AutoRAG等）
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"""
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from __future__ import annotations
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from typing import Dict, List, Any, Optional, Protocol, runtime_checkable
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@runtime_checkable
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class MemoryBankProtocol(Protocol):
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async def create_memory_bank(self, agent_name: str, display_name: Optional[str] = None) -> str: ...
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async def add_memory(
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self,
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agent_name: str,
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content: str,
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memory_type: str = "conversation",
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debate_topic: str = "",
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metadata: Optional[Dict[str, Any]] = None,
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) -> str: ...
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async def search_memories(
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self,
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agent_name: str,
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query: str,
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memory_type: Optional[str] = None,
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limit: int = 10,
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) -> List[Dict[str, Any]]: ...
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async def get_agent_context(self, agent_name: str, debate_topic: str) -> str: ...
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async def save_debate_session(
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self,
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debate_topic: str,
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participants: List[str],
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conversation_history: List[Dict[str, str]],
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outcomes: Optional[Dict[str, Any]] = None,
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) -> None: ...
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454
jixia_academy/core/memory_bank/cloudflare_memory_bank.py
Normal file
454
jixia_academy/core/memory_bank/cloudflare_memory_bank.py
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@@ -0,0 +1,454 @@
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#!/usr/bin/env python3
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"""
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Cloudflare AutoRAG Vectorize 记忆银行实现
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为稷下学宫AI辩论系统提供Cloudflare后端的记忆功能
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"""
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import os
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import json
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from typing import Dict, List, Optional, Any
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from dataclasses import dataclass
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from datetime import datetime
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import aiohttp
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from config.settings import get_cloudflare_config
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@dataclass
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class MemoryEntry:
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"""记忆条目数据结构"""
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id: str
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content: str
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metadata: Dict[str, Any]
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timestamp: str # ISO format string
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agent_name: str
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debate_topic: str
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memory_type: str # "conversation", "preference", "knowledge", "strategy"
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class CloudflareMemoryBank:
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"""
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Cloudflare AutoRAG Vectorize 记忆银行管理器
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利用Cloudflare Vectorize索引和Workers AI进行向量检索增强生成
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"""
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def __init__(self):
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"""初始化Cloudflare Memory Bank"""
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self.config = get_cloudflare_config()
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self.account_id = self.config['account_id']
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self.api_token = self.config['api_token']
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self.vectorize_index = self.config['vectorize_index']
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self.embed_model = self.config['embed_model']
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self.autorag_domain = self.config['autorag_domain']
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# 构建API基础URL
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self.base_url = f"https://api.cloudflare.com/client/v4/accounts/{self.account_id}"
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self.headers = {
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"Authorization": f"Bearer {self.api_token}",
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"Content-Type": "application/json"
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}
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# 八仙智能体名称映射
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self.baxian_agents = {
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"tieguaili": "铁拐李",
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"hanzhongli": "汉钟离",
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"zhangguolao": "张果老",
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"lancaihe": "蓝采和",
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"hexiangu": "何仙姑",
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"lvdongbin": "吕洞宾",
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"hanxiangzi": "韩湘子",
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"caoguojiu": "曹国舅"
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}
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async def _get_session(self) -> aiohttp.ClientSession:
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"""获取aiohttp会话"""
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return aiohttp.ClientSession()
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async def create_memory_bank(self, agent_name: str, display_name: str = None) -> str:
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"""
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为指定智能体创建记忆空间（在Cloudflare中通过命名空间或元数据实现）
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Args:
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agent_name: 智能体名称 (如 "tieguaili")
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display_name: 显示名称 (如 "铁拐李的记忆银行")
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Returns:
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记忆空间标识符 (这里用agent_name作为标识符)
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"""
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# Cloudflare Vectorize使用统一的索引，通过元数据区分不同智能体的记忆
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# 所以这里不需要实际创建，只需要返回标识符
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if not display_name:
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display_name = self.baxian_agents.get(agent_name, agent_name)
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print(f"✅ 为 {display_name} 准备Cloudflare记忆空间")
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return f"cf_memory_{agent_name}"
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async def add_memory(self,
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agent_name: str,
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content: str,
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memory_type: str = "conversation",
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debate_topic: str = "",
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metadata: Dict[str, Any] = None) -> str:
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"""
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添加记忆到Cloudflare Vectorize索引
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Args:
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agent_name: 智能体名称
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content: 记忆内容
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memory_type: 记忆类型 ("conversation", "preference", "knowledge", "strategy")
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debate_topic: 辩论主题
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metadata: 额外元数据
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Returns:
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记忆ID
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"""
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if metadata is None:
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metadata = {}
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# 生成记忆ID
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memory_id = f"mem_{agent_name}_{int(datetime.now().timestamp() * 1000000)}"
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# 构建记忆条目
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memory_entry = MemoryEntry(
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id=memory_id,
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content=content,
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metadata={
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**metadata,
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"agent_name": agent_name,
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"chinese_name": self.baxian_agents.get(agent_name, agent_name),
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"memory_type": memory_type,
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"debate_topic": debate_topic,
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"system": "jixia_academy"
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},
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timestamp=datetime.now().isoformat(),
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agent_name=agent_name,
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debate_topic=debate_topic,
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memory_type=memory_type
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)
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# 将记忆条目转换为JSON字符串，用于存储和检索
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memory_data = {
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"id": memory_id,
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"values": [], # 向量值将在嵌入时填充
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"metadata": memory_entry.metadata
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}
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try:
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# 1. 使用Workers AI生成嵌入向量
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embedding = await self._generate_embedding(content)
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memory_data["values"] = embedding
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# 2. 将记忆插入Vectorize索引
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async with await self._get_session() as session:
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url = f"{self.base_url}/vectorize/indexes/{self.vectorize_index}/upsert"
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payload = {
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"vectors": [memory_data]
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}
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async with session.post(url, headers=self.headers, json=payload) as response:
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if response.status == 200:
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result = await response.json()
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print(f"✅ 为 {self.baxian_agents.get(agent_name)} 添加记忆: {memory_type}")
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return memory_id
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else:
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error_text = await response.text()
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raise Exception(f"Failed to upsert memory: {response.status} - {error_text}")
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except Exception as e:
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print(f"❌ 添加记忆失败: {e}")
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raise
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async def _generate_embedding(self, text: str) -> List[float]:
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"""
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使用Cloudflare Workers AI生成文本嵌入
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Args:
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text: 要嵌入的文本
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Returns:
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嵌入向量
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"""
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async with await self._get_session() as session:
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url = f"{self.base_url}/ai/run/{self.embed_model}"
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payload = {
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"text": [text] # Workers AI embeddings API expects a list of texts
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}
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async with session.post(url, headers=self.headers, json=payload) as response:
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if response.status == 200:
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result = await response.json()
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# 提取嵌入向量 (通常是 result["result"]["data"][0]["embedding"])
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if "result" in result and "data" in result["result"] and len(result["result"]["data"]) > 0:
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return result["result"]["data"][0]["embedding"]
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else:
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raise Exception(f"Unexpected embedding response format: {result}")
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else:
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error_text = await response.text()
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raise Exception(f"Failed to generate embedding: {response.status} - {error_text}")
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async def search_memories(self,
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agent_name: str,
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query: str,
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memory_type: str = None,
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limit: int = 10) -> List[Dict[str, Any]]:
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"""
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使用向量相似性搜索智能体的相关记忆
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Args:
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agent_name: 智能体名称
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query: 搜索查询
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memory_type: 记忆类型过滤
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limit: 返回结果数量限制
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Returns:
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相关记忆列表
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"""
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try:
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# 1. 为查询生成嵌入向量
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query_embedding = await self._generate_embedding(query)
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# 2. 构建过滤条件
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filters = {
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"agent_name": agent_name
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}
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if memory_type:
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filters["memory_type"] = memory_type
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# 3. 执行向量搜索
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async with await self._get_session() as session:
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||||
url = f"{self.base_url}/vectorize/indexes/{self.vectorize_index}/query"
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||||
payload = {
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||||
"vector": query_embedding,
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"topK": limit,
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||||
"filter": filters,
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||||
"returnMetadata": True
|
||||
}
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||||
|
||||
async with session.post(url, headers=self.headers, json=payload) as response:
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||||
if response.status == 200:
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||||
result = await response.json()
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||||
matches = result.get("result", {}).get("matches", [])
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||||
|
||||
# 格式化返回结果
|
||||
memories = []
|
||||
for match in matches:
|
||||
memory_data = {
|
||||
"content": match["metadata"].get("content", ""),
|
||||
"metadata": match["metadata"],
|
||||
"relevance_score": match["score"]
|
||||
}
|
||||
memories.append(memory_data)
|
||||
|
||||
return memories
|
||||
else:
|
||||
error_text = await response.text()
|
||||
raise Exception(f"Failed to search memories: {response.status} - {error_text}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 搜索记忆失败: {e}")
|
||||
return []
|
||||
|
||||
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str:
|
||||
"""
|
||||
获取智能体在特定辩论主题下的上下文记忆
|
||||
|
||||
Args:
|
||||
agent_name: 智能体名称
|
||||
debate_topic: 辩论主题
|
||||
|
||||
Returns:
|
||||
格式化的上下文字符串
|
||||
"""
|
||||
# 搜索相关记忆
|
||||
conversation_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "conversation", limit=5
|
||||
)
|
||||
preference_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "preference", limit=3
|
||||
)
|
||||
strategy_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "strategy", limit=3
|
||||
)
|
||||
|
||||
# 构建上下文
|
||||
context_parts = []
|
||||
|
||||
if conversation_memories:
|
||||
context_parts.append("## 历史对话记忆")
|
||||
for mem in conversation_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
if preference_memories:
|
||||
context_parts.append("\n## 偏好记忆")
|
||||
for mem in preference_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
if strategy_memories:
|
||||
context_parts.append("\n## 策略记忆")
|
||||
for mem in strategy_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
if context_parts:
|
||||
return f"# {chinese_name}的记忆上下文\n\n" + "\n".join(context_parts)
|
||||
else:
|
||||
return f"# {chinese_name}的记忆上下文\n\n暂无相关记忆。"
|
||||
|
||||
async def save_debate_session(self,
|
||||
debate_topic: str,
|
||||
participants: List[str],
|
||||
conversation_history: List[Dict[str, str]],
|
||||
outcomes: Dict[str, Any] = None) -> None:
|
||||
"""
|
||||
保存完整的辩论会话到各参与者的记忆银行
|
||||
|
||||
Args:
|
||||
debate_topic: 辩论主题
|
||||
participants: 参与者列表
|
||||
conversation_history: 对话历史
|
||||
outcomes: 辩论结果和洞察
|
||||
"""
|
||||
for agent_name in participants:
|
||||
if agent_name not in self.baxian_agents:
|
||||
continue
|
||||
|
||||
# 保存对话历史
|
||||
conversation_summary = self._summarize_conversation(
|
||||
conversation_history, agent_name
|
||||
)
|
||||
await self.add_memory(
|
||||
agent_name=agent_name,
|
||||
content=conversation_summary,
|
||||
memory_type="conversation",
|
||||
debate_topic=debate_topic,
|
||||
metadata={
|
||||
"participants": participants,
|
||||
"session_length": len(conversation_history)
|
||||
}
|
||||
)
|
||||
|
||||
# 保存策略洞察
|
||||
if outcomes:
|
||||
strategy_insight = self._extract_strategy_insight(
|
||||
outcomes, agent_name
|
||||
)
|
||||
if strategy_insight:
|
||||
await self.add_memory(
|
||||
agent_name=agent_name,
|
||||
content=strategy_insight,
|
||||
memory_type="strategy",
|
||||
debate_topic=debate_topic,
|
||||
metadata={"session_outcome": outcomes}
|
||||
)
|
||||
|
||||
def _summarize_conversation(self,
|
||||
conversation_history: List[Dict[str, str]],
|
||||
agent_name: str) -> str:
|
||||
"""
|
||||
为特定智能体总结对话历史
|
||||
|
||||
Args:
|
||||
conversation_history: 对话历史
|
||||
agent_name: 智能体名称
|
||||
|
||||
Returns:
|
||||
对话总结
|
||||
"""
|
||||
agent_messages = [
|
||||
msg for msg in conversation_history
|
||||
if msg.get("agent") == agent_name
|
||||
]
|
||||
|
||||
if not agent_messages:
|
||||
return "本次辩论中未发言"
|
||||
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
summary = f"{chinese_name}在本次辩论中的主要观点：\n"
|
||||
|
||||
for i, msg in enumerate(agent_messages[:3], 1): # 只取前3条主要观点
|
||||
summary += f"{i}. {msg.get('content', '')[:100]}...\n"
|
||||
|
||||
return summary
|
||||
|
||||
def _extract_strategy_insight(self,
|
||||
outcomes: Dict[str, Any],
|
||||
agent_name: str) -> Optional[str]:
|
||||
"""
|
||||
从辩论结果中提取策略洞察
|
||||
|
||||
Args:
|
||||
outcomes: 辩论结果
|
||||
agent_name: 智能体名称
|
||||
|
||||
Returns:
|
||||
策略洞察或None
|
||||
"""
|
||||
# 这里可以根据实际的outcomes结构来提取洞察
|
||||
# 暂时返回一个简单的示例
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
|
||||
if "winner" in outcomes and outcomes["winner"] == agent_name:
|
||||
return f"{chinese_name}在本次辩论中获胜，其论证策略值得保持。"
|
||||
elif "insights" in outcomes and agent_name in outcomes["insights"]:
|
||||
return outcomes["insights"][agent_name]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# 便捷函数
|
||||
async def initialize_baxian_memory_banks() -> CloudflareMemoryBank:
|
||||
"""
|
||||
初始化所有八仙智能体的Cloudflare记忆空间
|
||||
|
||||
Returns:
|
||||
配置好的CloudflareMemoryBank实例
|
||||
"""
|
||||
memory_bank = CloudflareMemoryBank()
|
||||
|
||||
print("🏛️ 正在为稷下学宫八仙创建Cloudflare记忆空间...")
|
||||
|
||||
for agent_key, chinese_name in memory_bank.baxian_agents.items():
|
||||
try:
|
||||
await memory_bank.create_memory_bank(agent_key)
|
||||
except Exception as e:
|
||||
print(f"⚠️ 创建 {chinese_name} 记忆空间时出错: {e}")
|
||||
|
||||
print("✅ 八仙Cloudflare记忆空间初始化完成")
|
||||
return memory_bank
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
async def test_memory_bank():
|
||||
"""测试Cloudflare Memory Bank功能"""
|
||||
try:
|
||||
# 创建Memory Bank实例
|
||||
memory_bank = CloudflareMemoryBank()
|
||||
|
||||
# 测试创建记忆空间
|
||||
await memory_bank.create_memory_bank("tieguaili")
|
||||
|
||||
# 测试添加记忆
|
||||
await memory_bank.add_memory(
|
||||
agent_name="tieguaili",
|
||||
content="在讨论NVIDIA股票时，我倾向于逆向思维，关注潜在风险。",
|
||||
memory_type="preference",
|
||||
debate_topic="NVIDIA投资分析"
|
||||
)
|
||||
|
||||
# 测试搜索记忆
|
||||
results = await memory_bank.search_memories(
|
||||
agent_name="tieguaili",
|
||||
query="NVIDIA",
|
||||
limit=5
|
||||
)
|
||||
|
||||
print(f"搜索结果: {len(results)} 条记忆")
|
||||
for result in results:
|
||||
print(f"- {result['content']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 测试失败: {e}")
|
||||
|
||||
# 运行测试
|
||||
asyncio.run(test_memory_bank())
|
||||
23
jixia_academy/core/memory_bank/factory.py
Normal file
23
jixia_academy/core/memory_bank/factory.py
Normal file
@@ -0,0 +1,23 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
记忆银行工厂：根据配置创建不同后端实现
|
||||
"""
|
||||
from __future__ import annotations
|
||||
|
||||
import os
|
||||
from typing import Optional
|
||||
|
||||
from jixia_academy.core.memory_bank.interface import MemoryBankInterface
|
||||
|
||||
|
||||
def get_memory_backend(prefer: Optional[str] = None) -> MemoryBankInterface:
|
||||
"""
|
||||
获取记忆银行后端
|
||||
默认使用内存实现
|
||||
"""
|
||||
from jixia_academy.core.memory_bank.memory_impl import MemoryBankImpl
|
||||
|
||||
# 使用内存实现
|
||||
memory_bank = MemoryBankImpl(storage_path="jixia_academy/data/memory")
|
||||
print("🧠 使用内存记忆银行后端")
|
||||
return memory_bank
|
||||
106
jixia_academy/core/memory_bank/interface.py
Normal file
106
jixia_academy/core/memory_bank/interface.py
Normal file
@@ -0,0 +1,106 @@
|
||||
#!/usr/bin/env python3
|
||||
# -*- coding: utf-8 -*-
|
||||
"""
|
||||
记忆银行接口定义
|
||||
Memory Bank Interface Definition
|
||||
"""
|
||||
|
||||
from abc import ABC, abstractmethod
|
||||
from typing import List, Dict, Any, Optional
|
||||
from datetime import datetime
|
||||
|
||||
|
||||
class MemoryBankInterface(ABC):
|
||||
"""记忆银行接口"""
|
||||
|
||||
@abstractmethod
|
||||
async def initialize(self):
|
||||
"""初始化记忆银行"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def close(self):
|
||||
"""关闭记忆银行"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def add_debate_message(
|
||||
self,
|
||||
debate_id: str,
|
||||
speaker: str,
|
||||
message: str,
|
||||
round_num: int
|
||||
):
|
||||
"""添加辩论消息"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def get_debate_history(
|
||||
self,
|
||||
debate_id: str
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""获取辩论历史"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def save_debate_result(
|
||||
self,
|
||||
debate_id: str,
|
||||
summary: Dict[str, Any],
|
||||
participants: List[str]
|
||||
):
|
||||
"""保存辩论结果"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def get_debate_result(
|
||||
self,
|
||||
debate_id: str
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""获取辩论结果"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def list_debates(
|
||||
self,
|
||||
limit: int = 10
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""列出辩论"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def add_market_data(
|
||||
self,
|
||||
symbol: str,
|
||||
data: Dict[str, Any]
|
||||
):
|
||||
"""添加市场数据"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def get_market_data(
|
||||
self,
|
||||
symbol: str,
|
||||
start_date: datetime,
|
||||
end_date: datetime
|
||||
) -> List[Dict[str, Any]]:
|
||||
"""获取市场数据"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def store_analysis(
|
||||
self,
|
||||
analysis_id: str,
|
||||
analysis_type: str,
|
||||
content: Dict[str, Any]
|
||||
):
|
||||
"""存储分析结果"""
|
||||
pass
|
||||
|
||||
@abstractmethod
|
||||
async def get_analysis(
|
||||
self,
|
||||
analysis_id: str
|
||||
) -> Optional[Dict[str, Any]]:
|
||||
"""获取分析结果"""
|
||||
pass
|
||||
0
jixia_academy/core/memory_bank/memory_impl.py
Normal file
0
jixia_academy/core/memory_bank/memory_impl.py
Normal file
463
jixia_academy/core/memory_bank/vertex_memory_bank.py
Normal file
463
jixia_academy/core/memory_bank/vertex_memory_bank.py
Normal file
@@ -0,0 +1,463 @@
|
||||
#!/usr/bin/env python3
|
||||
"""
|
||||
Vertex AI Memory Bank 集成模块
|
||||
为稷下学宫AI辩论系统提供记忆银行功能
|
||||
"""
|
||||
|
||||
import os
|
||||
from typing import Dict, List, Optional, Any
|
||||
from dataclasses import dataclass
|
||||
from datetime import datetime
|
||||
import json
|
||||
|
||||
try:
|
||||
from google.cloud import aiplatform
|
||||
# Memory Bank 功能可能还在预览版中，先使用基础功能
|
||||
VERTEX_AI_AVAILABLE = True
|
||||
except ImportError:
|
||||
VERTEX_AI_AVAILABLE = False
|
||||
print("⚠️ Google Cloud AI Platform 未安装，Memory Bank功能不可用")
|
||||
print("安装命令: pip install google-cloud-aiplatform")
|
||||
|
||||
from config.settings import get_google_genai_config
|
||||
|
||||
|
||||
@dataclass
|
||||
class MemoryEntry:
|
||||
"""记忆条目数据结构"""
|
||||
content: str
|
||||
metadata: Dict[str, Any]
|
||||
timestamp: datetime
|
||||
agent_name: str
|
||||
debate_topic: str
|
||||
memory_type: str # "conversation", "preference", "knowledge", "strategy"
|
||||
|
||||
|
||||
class VertexMemoryBank:
|
||||
"""
|
||||
Vertex AI Memory Bank 管理器
|
||||
为八仙辩论系统提供智能记忆功能
|
||||
"""
|
||||
|
||||
def __init__(self, project_id: str, location: str = "us-central1"):
|
||||
"""
|
||||
初始化Memory Bank
|
||||
|
||||
Args:
|
||||
project_id: Google Cloud项目ID
|
||||
location: 部署区域
|
||||
"""
|
||||
if not VERTEX_AI_AVAILABLE:
|
||||
print("⚠️ Google Cloud AI Platform 未安装，使用本地模拟模式")
|
||||
# 不抛出异常，允许使用本地模拟模式
|
||||
|
||||
self.project_id = project_id
|
||||
self.location = location
|
||||
self.memory_banks = {} # 存储不同智能体的记忆银行
|
||||
self.local_memories = {} # 本地记忆存储 (临时方案)
|
||||
|
||||
# 初始化AI Platform
|
||||
try:
|
||||
aiplatform.init(project=project_id, location=location)
|
||||
print(f"✅ Vertex AI 初始化成功: {project_id} @ {location}")
|
||||
except Exception as e:
|
||||
print(f"⚠️ Vertex AI 初始化失败，使用本地模拟模式: {e}")
|
||||
|
||||
# 八仙智能体名称映射
|
||||
self.baxian_agents = {
|
||||
"tieguaili": "铁拐李",
|
||||
"hanzhongli": "汉钟离",
|
||||
"zhangguolao": "张果老",
|
||||
"lancaihe": "蓝采和",
|
||||
"hexiangu": "何仙姑",
|
||||
"lvdongbin": "吕洞宾",
|
||||
"hanxiangzi": "韩湘子",
|
||||
"caoguojiu": "曹国舅"
|
||||
}
|
||||
|
||||
@classmethod
|
||||
def from_config(cls) -> 'VertexMemoryBank':
|
||||
"""
|
||||
从配置创建Memory Bank实例
|
||||
|
||||
Returns:
|
||||
VertexMemoryBank实例
|
||||
"""
|
||||
config = get_google_genai_config()
|
||||
project_id = config.get('project_id')
|
||||
location = config.get('location', 'us-central1')
|
||||
|
||||
if not project_id:
|
||||
raise ValueError("Google Cloud Project ID 未配置，请设置 GOOGLE_CLOUD_PROJECT_ID")
|
||||
|
||||
return cls(project_id=project_id, location=location)
|
||||
|
||||
async def create_memory_bank(self, agent_name: str, display_name: str = None) -> str:
|
||||
"""
|
||||
为指定智能体创建记忆银行
|
||||
|
||||
Args:
|
||||
agent_name: 智能体名称 (如 "tieguaili")
|
||||
display_name: 显示名称 (如 "铁拐李的记忆银行")
|
||||
|
||||
Returns:
|
||||
记忆银行ID
|
||||
"""
|
||||
if not display_name:
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
display_name = f"{chinese_name}的记忆银行"
|
||||
|
||||
try:
|
||||
# 使用本地存储模拟记忆银行 (临时方案)
|
||||
memory_bank_id = f"memory_bank_{agent_name}_{self.project_id}"
|
||||
|
||||
# 初始化本地记忆存储
|
||||
if agent_name not in self.local_memories:
|
||||
self.local_memories[agent_name] = []
|
||||
|
||||
self.memory_banks[agent_name] = memory_bank_id
|
||||
print(f"✅ 为 {display_name} 创建记忆银行: {memory_bank_id}")
|
||||
|
||||
return memory_bank_id
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 创建记忆银行失败: {e}")
|
||||
raise
|
||||
|
||||
async def add_memory(self,
|
||||
agent_name: str,
|
||||
content: str,
|
||||
memory_type: str = "conversation",
|
||||
debate_topic: str = "",
|
||||
metadata: Dict[str, Any] = None) -> str:
|
||||
"""
|
||||
添加记忆到指定智能体的记忆银行
|
||||
|
||||
Args:
|
||||
agent_name: 智能体名称
|
||||
content: 记忆内容
|
||||
memory_type: 记忆类型 ("conversation", "preference", "knowledge", "strategy")
|
||||
debate_topic: 辩论主题
|
||||
metadata: 额外元数据
|
||||
|
||||
Returns:
|
||||
记忆ID
|
||||
"""
|
||||
if agent_name not in self.memory_banks:
|
||||
await self.create_memory_bank(agent_name)
|
||||
|
||||
if metadata is None:
|
||||
metadata = {}
|
||||
|
||||
# 构建记忆条目
|
||||
memory_entry = MemoryEntry(
|
||||
content=content,
|
||||
metadata={
|
||||
**metadata,
|
||||
"agent_name": agent_name,
|
||||
"chinese_name": self.baxian_agents.get(agent_name, agent_name),
|
||||
"memory_type": memory_type,
|
||||
"debate_topic": debate_topic,
|
||||
"system": "jixia_academy"
|
||||
},
|
||||
timestamp=datetime.now(),
|
||||
agent_name=agent_name,
|
||||
debate_topic=debate_topic,
|
||||
memory_type=memory_type
|
||||
)
|
||||
|
||||
try:
|
||||
# 使用本地存储添加记忆 (临时方案)
|
||||
memory_id = f"memory_{agent_name}_{len(self.local_memories[agent_name])}"
|
||||
|
||||
# 添加到本地存储
|
||||
memory_data = {
|
||||
"id": memory_id,
|
||||
"content": content,
|
||||
"metadata": memory_entry.metadata,
|
||||
"timestamp": memory_entry.timestamp.isoformat(),
|
||||
"memory_type": memory_type,
|
||||
"debate_topic": debate_topic
|
||||
}
|
||||
|
||||
self.local_memories[agent_name].append(memory_data)
|
||||
|
||||
print(f"✅ 为 {self.baxian_agents.get(agent_name)} 添加记忆: {memory_type}")
|
||||
return memory_id
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 添加记忆失败: {e}")
|
||||
raise
|
||||
|
||||
async def search_memories(self,
|
||||
agent_name: str,
|
||||
query: str,
|
||||
memory_type: str = None,
|
||||
limit: int = 10) -> List[Dict[str, Any]]:
|
||||
"""
|
||||
搜索智能体的相关记忆
|
||||
|
||||
Args:
|
||||
agent_name: 智能体名称
|
||||
query: 搜索查询
|
||||
memory_type: 记忆类型过滤
|
||||
limit: 返回结果数量限制
|
||||
|
||||
Returns:
|
||||
相关记忆列表
|
||||
"""
|
||||
if agent_name not in self.memory_banks:
|
||||
return []
|
||||
|
||||
try:
|
||||
# 使用本地存储搜索记忆 (临时方案)
|
||||
if agent_name not in self.local_memories:
|
||||
return []
|
||||
|
||||
memories = self.local_memories[agent_name]
|
||||
results = []
|
||||
|
||||
# 简单的文本匹配搜索
|
||||
query_lower = query.lower()
|
||||
|
||||
for memory in memories:
|
||||
# 检查记忆类型过滤
|
||||
if memory_type and memory.get("memory_type") != memory_type:
|
||||
continue
|
||||
|
||||
# 检查内容匹配
|
||||
content_lower = memory["content"].lower()
|
||||
debate_topic_lower = memory.get("debate_topic", "").lower()
|
||||
|
||||
# 在内容或辩论主题中搜索
|
||||
if query_lower in content_lower or query_lower in debate_topic_lower:
|
||||
# 计算简单的相关性分数
|
||||
content_matches = content_lower.count(query_lower)
|
||||
topic_matches = debate_topic_lower.count(query_lower)
|
||||
total_words = len(content_lower.split()) + len(debate_topic_lower.split())
|
||||
|
||||
relevance_score = (content_matches + topic_matches) / max(total_words, 1)
|
||||
|
||||
results.append({
|
||||
"content": memory["content"],
|
||||
"metadata": memory["metadata"],
|
||||
"relevance_score": relevance_score
|
||||
})
|
||||
|
||||
# 按相关性排序并限制结果数量
|
||||
results.sort(key=lambda x: x["relevance_score"], reverse=True)
|
||||
return results[:limit]
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 搜索记忆失败: {e}")
|
||||
return []
|
||||
|
||||
async def get_agent_context(self, agent_name: str, debate_topic: str) -> str:
|
||||
"""
|
||||
获取智能体在特定辩论主题下的上下文记忆
|
||||
|
||||
Args:
|
||||
agent_name: 智能体名称
|
||||
debate_topic: 辩论主题
|
||||
|
||||
Returns:
|
||||
格式化的上下文字符串
|
||||
"""
|
||||
# 搜索相关记忆
|
||||
conversation_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "conversation", limit=5
|
||||
)
|
||||
preference_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "preference", limit=3
|
||||
)
|
||||
strategy_memories = await self.search_memories(
|
||||
agent_name, debate_topic, "strategy", limit=3
|
||||
)
|
||||
|
||||
# 构建上下文
|
||||
context_parts = []
|
||||
|
||||
if conversation_memories:
|
||||
context_parts.append("## 历史对话记忆")
|
||||
for mem in conversation_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
if preference_memories:
|
||||
context_parts.append("\n## 偏好记忆")
|
||||
for mem in preference_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
if strategy_memories:
|
||||
context_parts.append("\n## 策略记忆")
|
||||
for mem in strategy_memories:
|
||||
context_parts.append(f"- {mem['content']}")
|
||||
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
if context_parts:
|
||||
return f"# {chinese_name}的记忆上下文\n\n" + "\n".join(context_parts)
|
||||
else:
|
||||
return f"# {chinese_name}的记忆上下文\n\n暂无相关记忆。"
|
||||
|
||||
async def save_debate_session(self,
|
||||
debate_topic: str,
|
||||
participants: List[str],
|
||||
conversation_history: List[Dict[str, str]],
|
||||
outcomes: Dict[str, Any] = None) -> None:
|
||||
"""
|
||||
保存完整的辩论会话到各参与者的记忆银行
|
||||
|
||||
Args:
|
||||
debate_topic: 辩论主题
|
||||
participants: 参与者列表
|
||||
conversation_history: 对话历史
|
||||
outcomes: 辩论结果和洞察
|
||||
"""
|
||||
for agent_name in participants:
|
||||
if agent_name not in self.baxian_agents:
|
||||
continue
|
||||
|
||||
# 保存对话历史
|
||||
conversation_summary = self._summarize_conversation(
|
||||
conversation_history, agent_name
|
||||
)
|
||||
await self.add_memory(
|
||||
agent_name=agent_name,
|
||||
content=conversation_summary,
|
||||
memory_type="conversation",
|
||||
debate_topic=debate_topic,
|
||||
metadata={
|
||||
"participants": participants,
|
||||
"session_length": len(conversation_history)
|
||||
}
|
||||
)
|
||||
|
||||
# 保存策略洞察
|
||||
if outcomes:
|
||||
strategy_insight = self._extract_strategy_insight(
|
||||
outcomes, agent_name
|
||||
)
|
||||
if strategy_insight:
|
||||
await self.add_memory(
|
||||
agent_name=agent_name,
|
||||
content=strategy_insight,
|
||||
memory_type="strategy",
|
||||
debate_topic=debate_topic,
|
||||
metadata={"session_outcome": outcomes}
|
||||
)
|
||||
|
||||
def _summarize_conversation(self,
|
||||
conversation_history: List[Dict[str, str]],
|
||||
agent_name: str) -> str:
|
||||
"""
|
||||
为特定智能体总结对话历史
|
||||
|
||||
Args:
|
||||
conversation_history: 对话历史
|
||||
agent_name: 智能体名称
|
||||
|
||||
Returns:
|
||||
对话总结
|
||||
"""
|
||||
agent_messages = [
|
||||
msg for msg in conversation_history
|
||||
if msg.get("agent") == agent_name
|
||||
]
|
||||
|
||||
if not agent_messages:
|
||||
return "本次辩论中未发言"
|
||||
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
summary = f"{chinese_name}在本次辩论中的主要观点：\n"
|
||||
|
||||
for i, msg in enumerate(agent_messages[:3], 1): # 只取前3条主要观点
|
||||
summary += f"{i}. {msg.get('content', '')[:100]}...\n"
|
||||
|
||||
return summary
|
||||
|
||||
def _extract_strategy_insight(self,
|
||||
outcomes: Dict[str, Any],
|
||||
agent_name: str) -> Optional[str]:
|
||||
"""
|
||||
从辩论结果中提取策略洞察
|
||||
|
||||
Args:
|
||||
outcomes: 辩论结果
|
||||
agent_name: 智能体名称
|
||||
|
||||
Returns:
|
||||
策略洞察或None
|
||||
"""
|
||||
# 这里可以根据实际的outcomes结构来提取洞察
|
||||
# 暂时返回一个简单的示例
|
||||
chinese_name = self.baxian_agents.get(agent_name, agent_name)
|
||||
|
||||
if "winner" in outcomes and outcomes["winner"] == agent_name:
|
||||
return f"{chinese_name}在本次辩论中获胜，其论证策略值得保持。"
|
||||
elif "insights" in outcomes and agent_name in outcomes["insights"]:
|
||||
return outcomes["insights"][agent_name]
|
||||
|
||||
return None
|
||||
|
||||
|
||||
# 便捷函数
|
||||
async def initialize_baxian_memory_banks(project_id: str, location: str = "us-central1") -> VertexMemoryBank:
|
||||
"""
|
||||
初始化所有八仙智能体的记忆银行
|
||||
|
||||
Args:
|
||||
project_id: Google Cloud项目ID
|
||||
location: 部署区域
|
||||
|
||||
Returns:
|
||||
配置好的VertexMemoryBank实例
|
||||
"""
|
||||
memory_bank = VertexMemoryBank(project_id, location)
|
||||
|
||||
print("🏛️ 正在为稷下学宫八仙创建记忆银行...")
|
||||
|
||||
for agent_key, chinese_name in memory_bank.baxian_agents.items():
|
||||
try:
|
||||
await memory_bank.create_memory_bank(agent_key)
|
||||
except Exception as e:
|
||||
print(f"⚠️ 创建 {chinese_name} 记忆银行时出错: {e}")
|
||||
|
||||
print("✅ 八仙记忆银行初始化完成")
|
||||
return memory_bank
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
import asyncio
|
||||
|
||||
async def test_memory_bank():
|
||||
"""测试Memory Bank功能"""
|
||||
try:
|
||||
# 从配置创建Memory Bank
|
||||
memory_bank = VertexMemoryBank.from_config()
|
||||
|
||||
# 测试创建记忆银行
|
||||
await memory_bank.create_memory_bank("tieguaili")
|
||||
|
||||
# 测试添加记忆
|
||||
await memory_bank.add_memory(
|
||||
agent_name="tieguaili",
|
||||
content="在讨论NVIDIA股票时，我倾向于逆向思维，关注潜在风险。",
|
||||
memory_type="preference",
|
||||
debate_topic="NVIDIA投资分析"
|
||||
)
|
||||
|
||||
# 测试搜索记忆
|
||||
results = await memory_bank.search_memories(
|
||||
agent_name="tieguaili",
|
||||
query="NVIDIA",
|
||||
limit=5
|
||||
)
|
||||
|
||||
print(f"搜索结果: {len(results)} 条记忆")
|
||||
for result in results:
|
||||
print(f"- {result['content']}")
|
||||
|
||||
except Exception as e:
|
||||
print(f"❌ 测试失败: {e}")
|
||||
|
||||
# 运行测试
|
||||
asyncio.run(test_memory_bank())
|
||||
Reference in New Issue
Block a user