feat: save

public/ai-agent-pattern-persistence/index.md

@@ -2,4 +2,147 @@
 
 AI 应用的状态持久化（Persistence）可以让用户在多轮交互中共享上下文能力，通过让 Agent 记住之前的互动，使用者可在多轮交互中建立连贯的对话，提供更具交互性和个性化的使用体验，从而更深入地探索主题或解决问题。
 
-langgraph
+持久化 Agent 可以实现功能：
+
+- 保存用户的多轮交互信息
+- 允许用户在不同阶段介入
+- 方便从长时间运行且易出错的任务中恢复到指定节点
+- 允许用户在不同的 checkpointer 间实现时间旅行（time travel）
+- ...
+
+## 实现方案
+
+基于 `langgraph` 的应用接入持久化能力，需要了解两个概念：
+
+1. checkpointer: 当前对话保存的形式，比如 memory（内存）或 sqliteSaver（关系数据库）
+2. thread: 当前对话的唯一 ID，历史会话检索需要基于上下文同一 thread
+
+以 MessageGraph 为例，我们尝试实现 Persistence:
+
+### 定义图结构
+
+```typescript
+import { MemorySaver } from "@langchain/langgraph";
+
+const graphState: StateGraphArgs<IState>["channels"] = {
+  messages: {
+    value: (x: BaseMessage[], y: BaseMessage[]) => x.concat(y),
+    default: () => [],
+  },
+};
+
+export async function callModel(state: IState): Promise<IState> {
+  const { messages } = state;
+  const response = await llm.invoke(messages);
+  return {
+    messages: [response],
+  };
+}
+
+const workflow = new StateGraph<IState>({
+  channels: graphState,
+})
+  .addNode("agent", callModel)
+  .addEdge(START, "agent")
+  .addEdge("agent", END);
+
+// 增加内存类型的 checkpointer
+// 后续运行实例生成的消息列表 messages 都会存储在内存中
+const graph = workflow.compile({ checkpointer: memory });
+```
+
+### 运行实例
+
+首先我们给出一个不带 threadId 的例子，运行两个独立实例:
+
+```typescript
+for await (const { messages } of await graph.stream(
+    { messages: [['user', "Hi I'm Yu, niced to meet you."]] },
+    {
+      streamMode: 'values',
+    }
+  )) {
+    let msg = messages[messages?.length - 1];
+    if (msg?.content) {
+      console.log(msg.content);
+    } else {
+      console.log(msg);
+    }
+  }
+
+  for await (const { messages } of await graph.stream(
+    { messages: [['user', 'Remember my name?']] },
+    {
+      streamMode: 'values',
+    }
+  )) {
+    let msg = messages[messages?.length - 1];
+    if (msg?.content) {
+      console.log(msg.content);
+    } else {
+      console.log(msg);
+    }
+  }
+}
+```
+
+结果输出：
+
+```shell
+[ 'user', "Hi I'm Yu, niced to meet you." ]
+Hello Yu, it's nice to meet you too. How can I assist you today?
+[ 'user', 'Remember my name?' ]
+I'm sorry, I don't have the ability to remember personal information about individuals.
+```
+
+给每个运行实例绑定同一个 thread_id，再次运行：
+
+```typescript
+// 基于 thread_id，将上下文对话记录作为同一个上下文保存
+let config = { configurable: { thread_id: "conversation-num-1" } };
+
+async function main() {
+  for await (const { messages } of await graph.stream(
+    { messages: [["user", "Hi I'm Yu, niced to meet you."]] },
+    // 上一个 threadId 对应的消息列表会作为下一个实例消息列表
+    {
+      ...config,
+      streamMode: "values",
+    }
+  )) {
+    let msg = messages[messages?.length - 1];
+    if (msg?.content) {
+      console.log(msg.content);
+    } else {
+      console.log(msg);
+    }
+  }
+
+  for await (const { messages } of await graph.stream(
+    { messages: [["user", "Remember my name?"]] },
+    // 上一个 threadId 对应的消息列表会作为下一个实例消息列表的初始消息注入
+    {
+      ...config,
+      streamMode: "values",
+    }
+  )) {
+    let msg = messages[messages?.length - 1];
+    if (msg?.content) {
+      console.log(msg.content);
+    } else {
+      console.log(msg);
+    }
+  }
+}
+
+main();
+```
+
+再次运行，此时输出结果如下：
+
+```shell
+[ 'user', "Hi I'm Yu, niced to meet you." ]
+Hello Yu, it's nice to meet you too. How can I assist you today?
+[ 'user', 'Remember my name?' ]
+Yes, I remember your name. It's Yu. How can I help you?
+```