Zep 是一个用于 AI 助手应用程序的长期记忆服务。 使用 Zep，您可以为 AI 助手提供回忆过去对话的能力，无论对话有多久远， 同时还可以减少幻觉、延迟和成本。
对 Zep Cloud 感兴趣？请参阅 Zep Cloud 安装指南注意：
ZepVectorStore 与 Documents 一起使用，旨在用作 Retriever。
它提供了与 Zep 的 ZepMemory 类分开的功能，后者旨在持久保存、丰富和搜索用户的聊天记录。
为什么选择 Zep 的 VectorStore？
Zep 使用 Zep 服务器本地的低延迟模型自动嵌入添加到 Zep 向量存储的文档。 Zep TS/JS 客户端可用于非 Node 边缘环境。这两个功能加上 Zep 的聊天记忆功能， 使 Zep 非常适合构建延迟和性能至关重要的对话式 LLM 应用程序。支持的搜索类型
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应用程序特别有用， 因为它可以重新排列结果以确保返回文档的多样性。安装
按照 Zep 开源快速入门指南 安装并开始使用 Zep。用法
您需要您的 Zep API URL 和（可选）API 密钥才能使用 Zep VectorStore。有关更多信息，请参阅 Zep 文档。 在下面的示例中，我们使用的是 Zep 的自动嵌入功能，该功能使用低延迟嵌入模型在 Zep 服务器上自动嵌入文档。由于 LangChain 需要传入Embeddings 实例，我们传入 FakeEmbeddings。
注意： 如果您传入除 FakeEmbeddings 以外的 Embeddings 实例，则将使用此类嵌入文档。您还必须将文档集合设置为 isAutoEmbedded === false。请参阅下面的 OpenAIEmbeddings 示例。
示例：从文档创建 ZepVectorStore 并查询
请参阅 此部分 以获取有关安装 LangChain 包的一般说明。
npm
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npm install @langchain/openai @langchain/community @langchain/core
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import { ZepVectorStore } from "@langchain/community/vectorstores/zep";
import { FakeEmbeddings } from "@langchain/core/utils/testing";
import { TextLoader } from "@langchain/classic/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));
};
示例：使用元数据过滤器查询 ZepVectorStore
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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${JSON.stringify(results)}`);
})
.catch((e) => {
if (e.name === "NotFoundError") {
console.log("No results found");
} else {
throw e;
}
});
};
示例：使用 LangChain 嵌入类，如 OpenAIEmbeddings
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import { ZepVectorStore } from "@langchain/community/vectorstores/zep";
import { OpenAIEmbeddings } from "@langchain/openai";
import { TextLoader } from "@langchain/classic/document_loaders/fs/text";
import { randomUUID } from "crypto";
const loader = new TextLoader("src/document_loaders/example_data/example.txt");
const docs = await loader.load();
export const run = async () => {
const collectionName = `collection${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));
};
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