> ## Documentation Index
> Fetch the complete documentation index at: https://nvd-54.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# 向量嵌入模型集成
> 使用 LangChain JavaScript 集成 embedding models。
## 概述
本概述涵盖**基于文本的向量嵌入模型**。LangChain 目前不支持多模态向量嵌入。
向量嵌入模型将原始文本（如句子、段落或推文）转换为一个固定长度的数字向量，以捕获其**语义含义**。这些向量使机器能够基于含义而非精确词汇来比较和搜索文本。
在实践中，这意味着具有相似含义的文本在向量空间中被放置在相近的位置。例如，向量嵌入不仅可以匹配 *"machine learning"* 这一短语，还可以找到讨论相关概念的文档，即使使用了不同的措辞。
### 工作原理
1. **Vectorization** — The model encodes each input string as a high-dimensional vector.
2. **Similarity scoring** — Vectors are compared using mathematical metrics to measure how closely related the underlying texts are.
### 相似度指标
通常使用以下几种指标来比较向量嵌入：
* **Cosine similarity** — measures the angle between two vectors.
* **Euclidean distance** — measures the straight-line distance between points.
* **Dot product** — measures how much one vector projects onto another.
## 接口
LangChain 通过 [Embeddings](https://reference.langchain.com/javascript/langchain-core/embeddings/Embeddings) 接口为文本向量嵌入模型（如 OpenAI、Cohere、Hugging Face）提供标准接口。
提供两个主要方法：
* `embedDocuments(documents: string[]) → number[][]`: Embeds a list of documents.
* `embedQuery(text: string) → number[]`: Embeds a single query.
该接口允许查询和文档使用不同的策略进行嵌入，尽管大多数提供商在实践中以相同方式处理它们。
## 安装和使用
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/openai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/openai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/openai
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
OPENAI_API_KEY=your-api-key
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { OpenAIEmbeddings } from "@langchain/openai";
const embeddings = new OpenAIEmbeddings({
model: "text-embedding-3-large"
});
```
安装依赖
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/openai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/openai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/openai
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
AZURE_OPENAI_API_INSTANCE_NAME=
AZURE_OPENAI_API_KEY=
AZURE_OPENAI_API_VERSION="2024-02-01"
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { AzureOpenAIEmbeddings } from "@langchain/openai";
const embeddings = new AzureOpenAIEmbeddings({
azureOpenAIApiEmbeddingsDeploymentName: "text-embedding-ada-002"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/aws
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/aws
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/aws
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
BEDROCK_AWS_REGION=your-region
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { BedrockEmbeddings } from "@langchain/aws";
const embeddings = new BedrockEmbeddings({
model: "amazon.titan-embed-text-v1"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/google-genai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/google-genai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/google-genai
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
GOOGLE_API_KEY=your-api-key
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { GoogleGenerativeAIEmbeddings } from "@langchain/google-genai";
const embeddings = new GoogleGenerativeAIEmbeddings({
model: "text-embedding-004"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/google-vertexai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/google-vertexai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/google-vertexai
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
GOOGLE_APPLICATION_CREDENTIALS=credentials.json
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { VertexAIEmbeddings } from "@langchain/google-vertexai";
const embeddings = new VertexAIEmbeddings({
model: "gemini-embedding-001"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/mistralai
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/mistralai
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/mistralai
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
MISTRAL_API_KEY=your-api-key
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { MistralAIEmbeddings } from "@langchain/mistralai";
const embeddings = new MistralAIEmbeddings({
model: "mistral-embed"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/cohere
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/cohere
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/cohere
```
添加环境变量：
```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
COHERE_API_KEY=your-api-key
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { CohereEmbeddings } from "@langchain/cohere";
const embeddings = new CohereEmbeddings({
model: "embed-english-v3.0"
});
```
安装依赖：
```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
npm i @langchain/ollama
```
```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
yarn add @langchain/ollama
```
```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
pnpm add @langchain/ollama
```
实例化模型：
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { OllamaEmbeddings } from "@langchain/ollama";
const embeddings = new OllamaEmbeddings({
model: "llama2",
baseUrl: "http://localhost:11434", // Default value
});
```
## 缓存
向量嵌入可以被存储或临时缓存，以避免重新计算。
可以使用 `CacheBackedEmbeddings` 来缓存向量嵌入。此包装器将向量嵌入存储在键值存储中，文本被哈希处理，哈希值用作缓存中的键。
The main supported way to initialize a `CacheBackedEmbeddings` is `fromBytesStore`. It takes the following parameters:
* **underlyingEmbeddings**: The embedder to use for embedding.
* **documentEmbeddingStore**: Any [`BaseStore`](/oss/javascript/integrations/stores/) for caching document embeddings.
* **options.namespace**: (optional, defaults to `""`) The namespace to use for the document cache. Helps avoid collisions (e.g., set it to the embedding model name).
- Always set the `namespace` parameter to avoid collisions when using different embedding models.
- `CacheBackedEmbeddings` does not cache query embeddings by default. To enable this, specify a `query_embedding_store`.
```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}}
import { CacheBackedEmbeddings } from "@langchain/classic/embeddings/cache_backed";
import { InMemoryStore } from "@langchain/core/stores";
const underlyingEmbeddings = new OpenAIEmbeddings();
const inMemoryStore = new InMemoryStore();
const cacheBackedEmbeddings = CacheBackedEmbeddings.fromBytesStore(
underlyingEmbeddings,
inMemoryStore,
{
namespace: underlyingEmbeddings.model,
}
);
// Example: caching a query embedding
const tic = Date.now();
const queryEmbedding = cacheBackedEmbeddings.embedQuery("Hello, world!");
console.log(`First call took: ${Date.now() - tic}ms`);
// Example: caching a document embedding
const tic = Date.now();
const documentEmbedding = cacheBackedEmbeddings.embedDocuments(["Hello, world!"]);
console.log(`Cached creation time: ${Date.now() - tic}ms`);
```
在生产环境中，你通常会使用更稳健的持久化存储，如数据库或云存储。请参阅[存储集成](/oss/javascript/integrations/stores/)了解可选方案。
## 所有集成
***
[将这些文档连接到](/use-these-docs) Claude、VSCode 等工具，通过 MCP 获取实时答案。
[在 GitHub 上编辑此页面](https://github.com/langchain-ai/docs/edit/main/src/oss/javascript/integrations/embeddings/index.mdx) 或 [提交 issue](https://github.com/langchain-ai/docs/issues/new/choose)。