Zep 开源

¥Zep Open Source

Zep 是一项面向 AI 助手应用的长期内存服务。使用 Zep，你可以为 AI 助手提供回忆过去对话的能力，无论对话有多久远，同时还能减少幻觉、延迟和成本。
¥Zep is a long-term memory service for AI Assistant apps. With Zep, you can provide AI assistants with the ability to recall past conversations, no matter how distant, while also reducing hallucinations, latency, and cost.

对 Zep Cloud 感兴趣？请参阅 Zep Cloud 安装指南
¥Interested in Zep Cloud? See Zep Cloud Installation Guide

注意：ZepVectorStore 与 Documents 配合使用，旨在用作 Retriever。它为 Zep 的 ZepMemory 类提供了独立的功能，该类旨在持久化、丰富和搜索用户的聊天记录。

¥Note: The ZepVectorStore works with Documents and is intended to be used as a Retriever. It offers separate functionality to Zep's ZepMemory class, which is designed for persisting, enriching and searching your user's chat history.

为什么选择 Zep 的 VectorStore？🤖🚀

¥Why Zep's VectorStore? 🤖🚀

Zep 使用 Zep 服务器本地的低延迟模型自动嵌入添加到 Zep 向量存储 (Zep Vector Store) 的文档。Zep TS/JS 客户端可以在非 Node 边缘环境中使用。这两个概念与 Zep 的聊天记忆功能相结合，使 Zep 成为构建会话式 LLM 应用的理想选择，因为在这些应用中延迟和性能至关重要。

¥Zep automatically embeds documents added to the Zep Vector Store using low-latency models local to the Zep server. The Zep TS/JS client can be used in non-Node edge environments. These two together with Zep's chat memory functionality make Zep ideal for building conversational LLM apps where latency and performance are important.

支持的搜索类型

¥Supported Search Types

Zep 支持相似性搜索和最大边际相关性 (MMR) 搜索。MMR 搜索对于检索增强生成 (RGA) 应用尤其有用，因为它会重新排序结果以确保返回文档的多样性。

¥Zep supports both similarity search and Maximal Marginal Relevance (MMR) search. MMR search is particularly useful for Retrieval Augmented Generation applications as it re-ranks results to ensure diversity in the returned documents.

安装

¥Installation

按照 Zep 开源快速入门指南的步骤安装并开始使用 Zep。

¥Follow the Zep Open Source Quickstart Guide to install and get started with Zep.

用法

¥Usage

你需要 Zep API URL 和可选的 API 密钥才能使用 Zep VectorStore。有关更多信息，请参阅 Zep 文档。

¥You'll need your Zep API URL and optionally an API key to use the Zep VectorStore. See the Zep docs for more information.

在下面的示例中，我们使用了 Zep 的自动嵌入功能，该功能使用低延迟嵌入模型自动将文档嵌入到 Zep 服务器上。由于 LangChain 需要传入 Embeddings 实例，因此我们传入了 FakeEmbeddings。

¥In the examples below, we're using Zep's auto-embedding feature which automatically embed documents on the Zep server using low-latency embedding models. Since LangChain requires passing in a Embeddings instance, we pass in FakeEmbeddings.

注意：如果你传入的是 FakeEmbeddings 以外的 Embeddings 实例，则此类将用于嵌入文档。你还必须将文档集合设置为 isAutoEmbedded === false。请参阅下面的 OpenAIEmbeddings 示例。

¥Note: If you pass in an Embeddings instance other than FakeEmbeddings, this class will be used to embed documents. You must also set your document collection to isAutoEmbedded === false. See the OpenAIEmbeddings example below.

示例：从文档和查询创建 ZepVectorStore

¥Example: Creating a ZepVectorStore from Documents & Querying

tip

请参阅此部分包含有关安装集成包的一般说明。

¥See this section for general instructions on installing integration packages.

npm
Yarn
pnpm

npm install @langchain/openai @langchain/community @langchain/core

yarn add @langchain/openai @langchain/community @langchain/core

pnpm add @langchain/openai @langchain/community @langchain/core

import { ZepVectorStore } from "@langchain/community/vectorstores/zep";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { randomUUID } from "crypto";

const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();
export const run = async () => {
  const collectionName = `collection${randomUUID().split("-")[0]}`;

  const zepConfig = {
    apiUrl: "http://localhost:8000", // this should be the URL of your Zep implementation
    collectionName,
    embeddingDimensions: 1536, // this much match the width of the embeddings you're using
    isAutoEmbedded: true, // If true, the vector store will automatically embed documents when they are added
  };

  const embeddings = new FakeEmbeddings();

  const vectorStore = await ZepVectorStore.fromDocuments(
    docs,
    embeddings,
    zepConfig
  );

  // Wait for the documents to be embedded
  // eslint-disable-next-line no-constant-condition
  while (true) {
    const c = await vectorStore.client.document.getCollection(collectionName);
    console.log(
      `Embedding status: ${c.document_embedded_count}/${c.document_count} documents embedded`
    );
    // eslint-disable-next-line no-promise-executor-return
    await new Promise((resolve) => setTimeout(resolve, 1000));
    if (c.status === "ready") {
      break;
    }
  }

  const results = await vectorStore.similaritySearchWithScore("bar", 3);

  console.log("Similarity Results:");
  console.log(JSON.stringify(results));

  const results2 = await vectorStore.maxMarginalRelevanceSearch("bar", {
    k: 3,
  });

  console.log("MMR Results:");
  console.log(JSON.stringify(results2));
};

API Reference:

ZepVectorStore from @langchain/community/vectorstores/zep
FakeEmbeddings from @langchain/core/utils/testing
TextLoader from langchain/document_loaders/fs/text

示例：使用元数据过滤器查询 ZepVectorStore

¥Example: Querying a ZepVectorStore using a metadata filter

import { ZepVectorStore } from "@langchain/community/vectorstores/zep";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { randomUUID } from "crypto";
import { Document } from "@langchain/core/documents";

const docs = [
  new Document({
    metadata: { album: "Led Zeppelin IV", year: 1971 },
    pageContent:
      "Stairway to Heaven is one of the most iconic songs by Led Zeppelin.",
  }),
  new Document({
    metadata: { album: "Led Zeppelin I", year: 1969 },
    pageContent:
      "Dazed and Confused was a standout track on Led Zeppelin's debut album.",
  }),
  new Document({
    metadata: { album: "Physical Graffiti", year: 1975 },
    pageContent:
      "Kashmir, from Physical Graffiti, showcases Led Zeppelin's unique blend of rock and world music.",
  }),
  new Document({
    metadata: { album: "Houses of the Holy", year: 1973 },
    pageContent:
      "The Rain Song is a beautiful, melancholic piece from Houses of the Holy.",
  }),
  new Document({
    metadata: { band: "Black Sabbath", album: "Paranoid", year: 1970 },
    pageContent:
      "Paranoid is Black Sabbath's second studio album and includes some of their most notable songs.",
  }),
  new Document({
    metadata: {
      band: "Iron Maiden",
      album: "The Number of the Beast",
      year: 1982,
    },
    pageContent:
      "The Number of the Beast is often considered Iron Maiden's best album.",
  }),
  new Document({
    metadata: { band: "Metallica", album: "Master of Puppets", year: 1986 },
    pageContent:
      "Master of Puppets is widely regarded as Metallica's finest work.",
  }),
  new Document({
    metadata: { band: "Megadeth", album: "Rust in Peace", year: 1990 },
    pageContent:
      "Rust in Peace is Megadeth's fourth studio album and features intricate guitar work.",
  }),
];

export const run = async () => {
  const collectionName = `collection${randomUUID().split("-")[0]}`;

  const zepConfig = {
    apiUrl: "http://localhost:8000", // this should be the URL of your Zep implementation
    collectionName,
    embeddingDimensions: 1536, // this much match the width of the embeddings you're using
    isAutoEmbedded: true, // If true, the vector store will automatically embed documents when they are added
  };

  const embeddings = new FakeEmbeddings();

  const vectorStore = await ZepVectorStore.fromDocuments(
    docs,
    embeddings,
    zepConfig
  );

  // Wait for the documents to be embedded
  // eslint-disable-next-line no-constant-condition
  while (true) {
    const c = await vectorStore.client.document.getCollection(collectionName);
    console.log(
      `Embedding status: ${c.document_embedded_count}/${c.document_count} documents embedded`
    );
    // eslint-disable-next-line no-promise-executor-return
    await new Promise((resolve) => setTimeout(resolve, 1000));
    if (c.status === "ready") {
      break;
    }
  }

  vectorStore
    .similaritySearchWithScore("sad music", 3, {
      where: { jsonpath: "$[*] ? (@.year == 1973)" }, // We should see a single result: The Rain Song
    })
    .then((results) => {
      console.log(`\n\nSimilarity Results:\n${JSON.stringify(results)}`);
    })
    .catch((e) => {
      if (e.name === "NotFoundError") {
        console.log("No results found");
      } else {
        throw e;
      }
    });

  // We're not filtering here, but rather demonstrating MMR at work.
  // We could also add a filter to the MMR search, as we did with the similarity search above.
  vectorStore
    .maxMarginalRelevanceSearch("sad music", {
      k: 3,
    })
    .then((results) => {
      console.log(`\n\nMMR Results:\n${JSON.stringify(results)}`);
    })
    .catch((e) => {
      if (e.name === "NotFoundError") {
        console.log("No results found");
      } else {
        throw e;
      }
    });
};

API Reference:

ZepVectorStore from @langchain/community/vectorstores/zep
FakeEmbeddings from @langchain/core/utils/testing
Document from @langchain/core/documents

示例：使用 LangChain 嵌入类，例如 `OpenAIEmbeddings`

¥Example: Using a LangChain Embedding Class such as OpenAIEmbeddings

import { ZepVectorStore } from "@langchain/community/vectorstores/zep";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "langchain/document_loaders/fs/text";
import { randomUUID } from "crypto";

const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();
export const run = async () => {
  const collectionName = `collection${randomUUID().split("-")[0]}`;

  const zepConfig = {
    apiUrl: "http://localhost:8000", // this should be the URL of your Zep implementation
    collectionName,
    embeddingDimensions: 1536, // this much match the width of the embeddings you're using
    isAutoEmbedded: false, // set to false to disable auto-embedding
  };

  const embeddings = new OpenAIEmbeddings();

  const vectorStore = await ZepVectorStore.fromDocuments(
    docs,
    embeddings,
    zepConfig
  );

  const results = await vectorStore.similaritySearchWithScore("bar", 3);

  console.log("Similarity Results:");
  console.log(JSON.stringify(results));

  const results2 = await vectorStore.maxMarginalRelevanceSearch("bar", {
    k: 3,
  });

  console.log("MMR Results:");
  console.log(JSON.stringify(results2));
};

API Reference:

ZepVectorStore from @langchain/community/vectorstores/zep
OpenAIEmbeddings from @langchain/openai
TextLoader from langchain/document_loaders/fs/text

¥Related

向量存储概念指南
¥Vector store conceptual guide
向量存储操作指南
¥Vector store how-to guides

Zep 开源

为什么选择 Zep 的 VectorStore？🤖🚀​

支持的搜索类型​

安装​

用法​

示例：从文档和查询创建 ZepVectorStore​

API Reference:

示例：使用元数据过滤器查询 ZepVectorStore​

API Reference:

示例：使用 LangChain 嵌入类，例如 OpenAIEmbeddings​

API Reference:

相关​

为什么选择 Zep 的 VectorStore？🤖🚀

支持的搜索类型

安装

用法

示例：从文档和查询创建 ZepVectorStore

示例：使用元数据过滤器查询 ZepVectorStore

示例：使用 LangChain 嵌入类，例如 `OpenAIEmbeddings`

相关