Zep 云内存

回忆、理解和提取聊天历史记录中的数据。为个性化的 AI 体验提供动力。

Zep 是一款面向 AI 助手应用程序的长期内存服务。使用 Zep，您可以为 AI 助手提供回忆过去对话的能力，无论这些对话多么遥远，同时还可以减少幻觉、延迟和成本。

Zep 云的工作原理

Zep 持久保存和回忆聊天历史记录，并自动从这些聊天历史记录中生成摘要和其他工件。它还嵌入消息和摘要，使您能够在 Zep 中搜索过去对话中的相关上下文。Zep 以异步方式执行所有这些操作，确保这些操作不会影响用户的聊天体验。数据被持久保存到数据库，允许您在增长需求时扩展。

Zep 还为文档向量搜索提供了一个简单易用的抽象，称为文档集合。这旨在补充 Zep 的核心内存功能，但并非旨在成为通用的向量数据库。

Zep 允许您更刻意地构建您的提示

自动添加一些最近的消息，您可以自定义应用程序的数字；
上述消息之前的最近对话的摘要；
和/或从整个聊天会话中显示的上下文相关的摘要或消息。
和/或来自 Zep 文档集合的相关业务数据。

Zep 云提供

事实提取：从对话中自动构建事实表，无需预先定义数据模式。
对话分类：即时准确地对聊天对话进行分类。了解用户的意图和情绪，细分用户等等。根据语义上下文路由链并触发事件。
结构化数据提取：使用您定义的模式，快速从聊天对话中提取业务数据。了解您的助手应该询问什么才能完成其任务。

安装

按照 Zep 云 Typescript SDK 安装指南来安装并开始使用 Zep。

您需要您的 Zep 云项目 API 密钥才能使用 Zep 云内存。有关更多信息，请参阅 Zep 云文档。

提示

查看此部分以获取有关安装集成软件包的一般说明。

npm
Yarn
pnpm

npm install @getzep/zep-cloud @langchain/openai @langchain/community

yarn add @getzep/zep-cloud @langchain/openai @langchain/community

pnpm add @getzep/zep-cloud @langchain/openai @langchain/community

ZepCloudChatMessageHistory + RunnableWithMessageHistory 用法

import { ZepClient } from "@getzep/zep-cloud";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { ChatOpenAI } from "@langchain/openai";
import { RunnableWithMessageHistory } from "@langchain/core/runnables";
import { ZepCloudChatMessageHistory } from "@langchain/community/stores/message/zep_cloud";

// Your Zep Session ID.
const sessionId = "<Zep Session ID>";
const zepClient = new ZepClient({
  // Your Zep Cloud Project API key https://help.getzep.com/projects
  apiKey: "<Zep Api Key>",
});

const prompt = ChatPromptTemplate.fromMessages([
  ["system", "Answer the user's question below. Be polite and helpful:"],
  new MessagesPlaceholder("history"),
  ["human", "{question}"],
]);

const chain = prompt
  .pipe(
    new ChatOpenAI({
      temperature: 0.8,
      modelName: "gpt-3.5-turbo-1106",
    })
  )
  .withConfig({
    callbacks: [new ConsoleCallbackHandler()],
  });

const chainWithHistory = new RunnableWithMessageHistory({
  runnable: chain,
  getMessageHistory: (sessionId) =>
    new ZepCloudChatMessageHistory({
      client: zepClient,
      sessionId,
      memoryType: "perpetual",
    }),
  inputMessagesKey: "question",
  historyMessagesKey: "history",
});

const result = await chainWithHistory.invoke(
  {
    question: "What did we talk about earlier?",
  },
  {
    configurable: {
      sessionId,
    },
  }
);

console.log("result", result);

API 参考

ChatPromptTemplate 来自 @langchain/core/prompts
MessagesPlaceholder 来自 @langchain/core/prompts
ConsoleCallbackHandler 来自 @langchain/core/tracers/console
ChatOpenAI 来自 @langchain/openai
RunnableWithMessageHistory 来自 @langchain/core/runnables
ZepCloudChatMessageHistory 来自 @langchain/community/stores/message/zep_cloud

ZepCloudChatMessageHistory + RunnableWithMessageHistory + ZepVectorStore（作为检索器）用法

import { ZepClient } from "@getzep/zep-cloud";
import {
  ChatPromptTemplate,
  MessagesPlaceholder,
} from "@langchain/core/prompts";
import { ConsoleCallbackHandler } from "@langchain/core/tracers/console";
import { ChatOpenAI } from "@langchain/openai";
import { Document } from "@langchain/core/documents";
import {
  RunnableLambda,
  RunnableMap,
  RunnablePassthrough,
  RunnableWithMessageHistory,
} from "@langchain/core/runnables";
import { ZepCloudVectorStore } from "@langchain/community/vectorstores/zep_cloud";
import { StringOutputParser } from "@langchain/core/output_parsers";
import { ZepCloudChatMessageHistory } from "@langchain/community/stores/message/zep_cloud";

interface ChainInput {
  question: string;
  sessionId: string;
}

async function combineDocuments(docs: Document[], documentSeparator = "\n\n") {
  const docStrings: string[] = await Promise.all(
    docs.map((doc) => doc.pageContent)
  );
  return docStrings.join(documentSeparator);
}

// Your Zep Session ID.
const sessionId = "<Zep Session ID>";

const collectionName = "<Zep Collection Name>";

const zepClient = new ZepClient({
  // Your Zep Cloud Project API key https://help.getzep.com/projects
  apiKey: "<Zep Api Key>",
});

const vectorStore = await ZepCloudVectorStore.init({
  client: zepClient,
  collectionName,
});

const prompt = ChatPromptTemplate.fromMessages([
  [
    "system",
    `Answer the question based only on the following context and conversation history: {context}`,
  ],
  new MessagesPlaceholder("history"),
  ["human", "{question}"],
]);

const model = new ChatOpenAI({
  temperature: 0.8,
  modelName: "gpt-3.5-turbo-1106",
});
const retriever = vectorStore.asRetriever();
const searchQuery = new RunnableLambda({
  func: async (input: any) => {
    // You can use zep to synthesize a question based on the user input and session context.
    // It can be useful because sometimes the user will type something like "yes" or "ok", which is not very useful for vector store retrieval.
    const { question } = await zepClient.memory.synthesizeQuestion(
      input.session_id
    );
    console.log("Synthesized question: ", question);
    return question;
  },
});
const retrieverLambda = new RunnableLambda({
  func: async (question: string) => {
    const response = await retriever.invoke(question);
    return combineDocuments(response);
  },
});
const setupAndRetrieval = RunnableMap.from({
  context: searchQuery.pipe(retrieverLambda),
  question: (x: any) => x.question,
  history: (x: any) => x.history,
});
const outputParser = new StringOutputParser();

const ragChain = setupAndRetrieval.pipe(prompt).pipe(model).pipe(outputParser);

const invokeChain = (chainInput: ChainInput) => {
  const chainWithHistory = new RunnableWithMessageHistory({
    runnable: RunnablePassthrough.assign({
      session_id: () => chainInput.sessionId,
    }).pipe(ragChain),
    getMessageHistory: (sessionId) =>
      new ZepCloudChatMessageHistory({
        client: zepClient,
        sessionId,
        memoryType: "perpetual",
      }),
    inputMessagesKey: "question",
    historyMessagesKey: "history",
  });

  return chainWithHistory.invoke(
    { question: chainInput.question },
    {
      configurable: {
        sessionId: chainInput.sessionId,
      },
    }
  );
};

const chain = new RunnableLambda({
  func: invokeChain,
}).withConfig({
  callbacks: [new ConsoleCallbackHandler()],
});

const result = await chain.invoke({
  question: "Project Gutenberg",
  sessionId,
});

console.log("result", result);

API 参考

ChatPromptTemplate 来自 @langchain/core/prompts
MessagesPlaceholder 来自 @langchain/core/prompts
ConsoleCallbackHandler 来自 @langchain/core/tracers/console
ChatOpenAI 来自 @langchain/openai
Document 来自 @langchain/core/documents
RunnableLambda 来自 @langchain/core/runnables
RunnableMap 来自 @langchain/core/runnables
RunnablePassthrough 来自 @langchain/core/runnables
RunnableWithMessageHistory 来自 @langchain/core/runnables
ZepCloudVectorStore 来自 @langchain/community/vectorstores/zep_cloud
StringOutputParser 来自 @langchain/core/output_parsers
ZepCloudChatMessageHistory 来自 @langchain/community/stores/message/zep_cloud

内存使用情况

import { ChatOpenAI } from "@langchain/openai";
import { ConversationChain } from "langchain/chains";
import { ZepCloudMemory } from "@langchain/community/memory/zep_cloud";
import { randomUUID } from "crypto";

const sessionId = randomUUID(); // This should be unique for each user or each user's session.

const memory = new ZepCloudMemory({
  sessionId,
  // Your Zep Cloud Project API key https://help.getzep.com/projects
  apiKey: "<Zep Api Key>",
});

const model = new ChatOpenAI({
  modelName: "gpt-3.5-turbo",
  temperature: 0,
});

const chain = new ConversationChain({ llm: model, memory });
console.log("Memory Keys:", memory.memoryKeys);

const res1 = await chain.invoke({ input: "Hi! I'm Jim." });
console.log({ res1 });
/*
{
  res1: {
    text: "Hello Jim! It's nice to meet you. My name is AI. How may I assist you today?"
  }
}
*/

const res2 = await chain.invoke({ input: "What did I just say my name was?" });
console.log({ res2 });

/*
{
  res1: {
    text: "You said your name was Jim."
  }
}
*/
console.log("Session ID: ", sessionId);
console.log("Memory: ", await memory.loadMemoryVariables({}));

API 参考

ChatOpenAI 来自 @langchain/openai
ConversationChain 来自 langchain/chains
ZepCloudMemory 来自 @langchain/community/memory/zep_cloud

Zep 云内存

Zep 云的工作原理

安装

ZepCloudChatMessageHistory + RunnableWithMessageHistory 用法

API 参考

ZepCloudChatMessageHistory + RunnableWithMessageHistory + ZepVectorStore（作为检索器）用法

API 参考

内存使用情况

API 参考

此页面是否有帮助？

您也可以在 GitHub 上留下详细的反馈 GitHub.

Zep 云内存

Zep 云的工作原理​

安装​

ZepCloudChatMessageHistory + RunnableWithMessageHistory 用法​

API 参考

ZepCloudChatMessageHistory + RunnableWithMessageHistory + ZepVectorStore（作为检索器）用法​

API 参考

内存使用情况​

API 参考

此页面是否有帮助？

您也可以在 GitHub 上留下详细的反馈 GitHub.

Zep 云的工作原理

安装

ZepCloudChatMessageHistory + RunnableWithMessageHistory 用法

ZepCloudChatMessageHistory + RunnableWithMessageHistory + ZepVectorStore（作为检索器）用法

内存使用情况