TogetherAIEmbeddings

这将帮助你开始使用 TogetherAIEmbeddings 嵌入模型 与 LangChain。有关 TogetherAIEmbeddings 功能和配置选项的详细文档，请参阅 API 参考。

概述

集成详细信息

类	包	本地	Py 支持	包下载	包最新
TogetherAIEmbeddings	@langchain/community	❌	✅

设置

要访问 TogetherAI 嵌入模型，你需要创建一个 TogetherAI 帐户，获取 API 密钥，并安装 @langchain/community 集成包。

凭据

你可以在 此处 注册 Together 帐户并创建 API 密钥。完成此操作后，设置 TOGETHER_AI_API_KEY 环境变量

export TOGETHER_AI_API_KEY="your-api-key"

如果你想获取模型调用的自动跟踪，你还可以通过取消以下注释来设置你的 LangSmith API 密钥

# export LANGCHAIN_TRACING_V2="true"
# export LANGCHAIN_API_KEY="your-api-key"

安装

LangChain TogetherAIEmbeddings 集成位于 @langchain/community 包中

提示

查看 此部分以获取有关安装集成包的一般说明。

npm

npm i @langchain/community

yarn

yarn add @langchain/community

pnpm

pnpm add @langchain/community

实例化

现在我们可以实例化模型对象并生成聊天完成内容

import { TogetherAIEmbeddings } from "@langchain/community/embeddings/togetherai";

const embeddings = new TogetherAIEmbeddings({
  model: "togethercomputer/m2-bert-80M-8k-retrieval", // Default value
});

索引和检索

嵌入模型通常用于检索增强型生成 (RAG) 流，作为索引数据的组成部分，以及以后检索数据。有关更详细的说明，请参阅我们位于 使用外部知识教程 下的 RAG 教程。

下面，请参阅如何使用我们上面初始化的 embeddings 对象索引和检索数据。在此示例中，我们将使用演示 MemoryVectorStore 来索引和检索示例文档。

// Create a vector store with a sample text
import { MemoryVectorStore } from "langchain/vectorstores/memory";

const text =
  "LangChain is the framework for building context-aware reasoning applications";

const vectorstore = await MemoryVectorStore.fromDocuments(
  [{ pageContent: text, metadata: {} }],
  embeddings
);

// Use the vector store as a retriever that returns a single document
const retriever = vectorstore.asRetriever(1);

// Retrieve the most similar text
const retrievedDocuments = await retriever.invoke("What is LangChain?");

retrievedDocuments[0].pageContent;

LangChain is the framework for building context-aware reasoning applications

直接使用

在幕后，向量存储和检索器实现会调用 embeddings.embedDocument(...) 和 embeddings.embedQuery(...) 来创建用于 fromDocuments 中的文本以及检索器 invoke 操作的文本的嵌入。

你可以直接调用这些方法来获取你自己的用例的嵌入。

嵌入单个文本

你可以使用 embedQuery 来嵌入查询以进行搜索。这将生成一个特定于查询的向量表示

const singleVector = await embeddings.embedQuery(text);

console.log(singleVector.slice(0, 100));

[
     0.3812227, -0.052848946,  -0.10564975,   0.03480297,    0.2878488,
  0.0084609175,   0.11605915,   0.05303011,   0.14711718,  -0.14407106,
   -0.29865336,  -0.15807179, -0.068397366,   -0.2708063,  0.056596708,
   -0.07656515,  0.052995138,  -0.11275427,  0.028096694,  0.123501234,
  -0.039519835,   0.12148692,  -0.12820457,   0.15691335,  0.033519063,
   -0.27026987,  -0.08460162,  -0.23792154,    -0.234982,  -0.05786798,
   0.016467346,  -0.17168592, -0.060787182,  0.038752213,  -0.08169927,
    0.09327062,   0.29490772,    0.0167866,  -0.32224452,   -0.2037822,
   -0.10284172, -0.124050565,   0.25344968,  -0.06275548,  -0.14180769,
  0.0046709594,  0.073105976,   0.12004031,   0.19224276, -0.022589967,
   0.102790825,    0.1138286, -0.057701062, -0.050010648,   -0.1632584,
   -0.18942119,  -0.12018798,   0.15288158,   0.07941474,   0.10440051,
   -0.13257962,  -0.19282033,  0.044656333,   0.13560675, -0.068929024,
   0.028590716,  0.055663664,   0.04652713,  0.014936657,  0.120679885,
   0.053866718,  -0.16296014,  0.119450666,  -0.29559663,  0.008097747,
    0.07380408,  -0.09010084,   -0.0687739,  -0.08575685,  -0.07202606,
    0.18868081,  -0.08392917,  0.014016109,   0.15435852, -0.030115498,
   -0.16927013,   0.02836557, -0.050763763,    0.0840437,  -0.22718845,
   0.111397505,  0.033395614, -0.123287566,   -0.2111604,   -0.1580479,
    0.05520573,   -0.1422921,   0.08828953,  0.051058788,  -0.13312188
]

嵌入多个文本

你可以使用 embedDocuments 来嵌入多个文本以进行索引。此方法内部使用的机制可能（但并非必须）与嵌入查询不同

const text2 =
  "LangGraph is a library for building stateful, multi-actor applications with LLMs";

const vectors = await embeddings.embedDocuments([text, text2]);

console.log(vectors[0].slice(0, 100));
console.log(vectors[1].slice(0, 100));

[
     0.3812227, -0.052848946,  -0.10564975,   0.03480297,    0.2878488,
  0.0084609175,   0.11605915,   0.05303011,   0.14711718,  -0.14407106,
   -0.29865336,  -0.15807179, -0.068397366,   -0.2708063,  0.056596708,
   -0.07656515,  0.052995138,  -0.11275427,  0.028096694,  0.123501234,
  -0.039519835,   0.12148692,  -0.12820457,   0.15691335,  0.033519063,
   -0.27026987,  -0.08460162,  -0.23792154,    -0.234982,  -0.05786798,
   0.016467346,  -0.17168592, -0.060787182,  0.038752213,  -0.08169927,
    0.09327062,   0.29490772,    0.0167866,  -0.32224452,   -0.2037822,
   -0.10284172, -0.124050565,   0.25344968,  -0.06275548,  -0.14180769,
  0.0046709594,  0.073105976,   0.12004031,   0.19224276, -0.022589967,
   0.102790825,    0.1138286, -0.057701062, -0.050010648,   -0.1632584,
   -0.18942119,  -0.12018798,   0.15288158,   0.07941474,   0.10440051,
   -0.13257962,  -0.19282033,  0.044656333,   0.13560675, -0.068929024,
   0.028590716,  0.055663664,   0.04652713,  0.014936657,  0.120679885,
   0.053866718,  -0.16296014,  0.119450666,  -0.29559663,  0.008097747,
    0.07380408,  -0.09010084,   -0.0687739,  -0.08575685,  -0.07202606,
    0.18868081,  -0.08392917,  0.014016109,   0.15435852, -0.030115498,
   -0.16927013,   0.02836557, -0.050763763,    0.0840437,  -0.22718845,
   0.111397505,  0.033395614, -0.123287566,   -0.2111604,   -0.1580479,
    0.05520573,   -0.1422921,   0.08828953,  0.051058788,  -0.13312188
]
[
   0.066308185, -0.032866564,  0.115751594,   0.19082588,      0.14017,
   -0.26976448, -0.056340694,  -0.26923394,    0.2548541,  -0.27271318,
    -0.2244126,   0.07949589,  -0.27710953,  -0.17993368,   0.09681616,
   -0.08692256,   0.22127126,  -0.14512022,  -0.18016525,   0.14892976,
    -0.0526347, -0.008140617,   -0.2916987,   0.23706906,  -0.38488507,
   -0.35881752,   0.09276949,  -0.07051063,  -0.07778231,   0.12552947,
    0.06256748,  -0.25832427,  0.025054429,   -0.1451448,   -0.2662871,
    0.13676351,  -0.07413256,   0.14966589,  -0.39968985,   0.15542287,
   -0.13107607,   0.02761394,  0.108077586,  -0.12076956,     0.128296,
   -0.05625126,   0.15723586, -0.056932643,   0.23720805,   0.23993455,
  -0.035553705, -0.053907514,  -0.11852807,   0.07005695,  -0.06317475,
   0.070009425,     0.284697,    0.2212059,  0.018890115,   0.16924675,
    0.21651487,   0.07259682,    0.1328156,    0.3261852,    0.1914124,
   -0.10120423,   0.03450111,  -0.22588971,  -0.04458192,   0.24116798,
  -0.021830376,  -0.30731413,   0.08586451, -0.058835756, 0.0010347435,
  0.0031927782,  -0.09403646,  -0.22608931,   0.15865424,   0.15738021,
    0.23582733,    0.1714161,    0.1585189,  -0.18085755,  0.019376995,
  -0.026587496, -0.017079154,  -0.04588549, -0.047336094, -0.082413346,
    -0.1114185,  -0.05403556,   0.12438637,  -0.20476522,     0.073182,
   -0.12210378, -0.010543863,  -0.09767598,    0.1057683, -0.050204434
]

API 参考

有关所有 TogetherAIEmbeddings 功能和配置的详细文档，请访问 API 参考：https://api.js.langchain.com/classes/langchain_community_embeddings_togetherai.TogetherAIEmbeddings.html

相关

• 嵌入模型 概念指南
• 嵌入模型 操作指南