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如何缓存嵌入结果

预备知识

本指南假设您熟悉以下概念：

可以存储或临时缓存嵌入结果以避免重复计算它们。

可以使用 CacheBackedEmbeddings 实例来实现嵌入缓存。

缓存支持的嵌入器是一个封装了嵌入器的包装器，它将嵌入结果缓存在键值存储中。

文本会被哈希处理，并且该哈希值将作为缓存中的键使用。

初始化 CacheBackedEmbeddings 的主要支持方式是使用 fromBytesStore 静态方法。它接受以下参数：

  • underlyingEmbeddings: 要使用的嵌入模型。
  • documentEmbeddingCache: 用于存储文档嵌入的缓存。
  • namespace: (可选，默认为 "") 文档缓存使用的命名空间。该命名空间用于避免与其他缓存发生冲突。例如，您可以将其设置为所使用的嵌入模型的名称。

注意： 请务必设置命名空间参数，以避免使用不同嵌入模型对相同文本进行嵌入时发生冲突。

内存缓存​

:::提示 请参阅安装集成包的一般说明部分。 :::

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

以下是一个使用内存缓存的基本测试示例。这种类型的缓存主要用于单元测试或原型设计。 如果您需要长期存储嵌入结果，请不要使用此缓存：

import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { InMemoryStore } from "@langchain/core/stores";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { TextLoader } from "langchain/document_loaders/fs/text";

const underlyingEmbeddings = new OpenAIEmbeddings();

const inMemoryStore = new InMemoryStore();

const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
  underlyingEmbeddings,
  inMemoryStore,
  {
    namespace: underlyingEmbeddings.model,
  }
);

const loader = new TextLoader("./state_of_the_union.txt");
const rawDocuments = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 0,
});
const documents = await splitter.splitDocuments(rawDocuments);

// No keys logged yet since the cache is empty
for await (const key of inMemoryStore.yieldKeys()) {
  console.log(key);
}

let time = Date.now();
const vectorstore = await FaissStore.fromDocuments(
  documents,
  cacheBackedEmbeddings
);
console.log(`Initial creation time: ${Date.now() - time}ms`);
/*
  Initial creation time: 1905ms
*/

// The second time is much faster since the embeddings for the input docs have already been added to the cache
time = Date.now();
const vectorstore2 = await FaissStore.fromDocuments(
  documents,
  cacheBackedEmbeddings
);
console.log(`Cached creation time: ${Date.now() - time}ms`);
/*
  Cached creation time: 8ms
*/

// Many keys logged with hashed values
const keys = [];
for await (const key of inMemoryStore.yieldKeys()) {
  keys.push(key);
}

console.log(keys.slice(0, 5));
/*
  [
    'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64',
    'text-embedding-ada-0023b424f5ed1271a6f5601add17c1b58b7c992772e',
    'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111',
    'text-embedding-ada-00262f72e0c2d711c6b861714ee624b28af639fdb13',
    'text-embedding-ada-00262d58882330038a4e6e25ea69a938f4391541874'
  ]
*/

API Reference:

Redis​

以下是一个使用 Redis 缓存的示例。

您首先需要安装 ioredis 作为对等依赖项，并传入一个已初始化的客户端：

npm install ioredis
import { Redis } from "ioredis";

import { OpenAIEmbeddings } from "@langchain/openai";
import { CacheBackedEmbeddings } from "langchain/embeddings/cache_backed";
import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";
import { FaissStore } from "@langchain/community/vectorstores/faiss";
import { RedisByteStore } from "@langchain/community/storage/ioredis";
import { TextLoader } from "langchain/document_loaders/fs/text";

const underlyingEmbeddings = new OpenAIEmbeddings();

// Requires a Redis instance running at http://localhost:6379.
// See https://github.com/redis/ioredis for full config options.
const redisClient = new Redis();
const redisStore = new RedisByteStore({
  client: redisClient,
});

const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
  underlyingEmbeddings,
  redisStore,
  {
    namespace: underlyingEmbeddings.model,
  }
);

const loader = new TextLoader("./state_of_the_union.txt");
const rawDocuments = await loader.load();
const splitter = new RecursiveCharacterTextSplitter({
  chunkSize: 1000,
  chunkOverlap: 0,
});
const documents = await splitter.splitDocuments(rawDocuments);

let time = Date.now();
const vectorstore = await FaissStore.fromDocuments(
  documents,
  cacheBackedEmbeddings
);
console.log(`Initial creation time: ${Date.now() - time}ms`);
/*
  Initial creation time: 1808ms
*/

// The second time is much faster since the embeddings for the input docs have already been added to the cache
time = Date.now();
const vectorstore2 = await FaissStore.fromDocuments(
  documents,
  cacheBackedEmbeddings
);
console.log(`Cached creation time: ${Date.now() - time}ms`);
/*
  Cached creation time: 33ms
*/

// Many keys logged with hashed values
const keys = [];
for await (const key of redisStore.yieldKeys()) {
  keys.push(key);
}

console.log(keys.slice(0, 5));
/*
  [
    'text-embedding-ada-002fa9ac80e1bf226b7b4dfc03ea743289a65a727b2',
    'text-embedding-ada-0027dbf9c4b36e12fe1768300f145f4640342daaf22',
    'text-embedding-ada-002ea9b59e760e64bec6ee9097b5a06b0d91cb3ab64',
    'text-embedding-ada-002fec5d021611e1527297c5e8f485876ea82dcb111',
    'text-embedding-ada-002c00f818c345da13fed9f2697b4b689338143c8c7'
  ]
*/

API Reference:

下一步​

现在您已经学会了如何使用缓存来避免重复计算嵌入结果。

接下来，请查看完整的 检索增强生成教程。

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