MistralAIEmbeddings

这将帮助您开始使用 MistralAIEmbeddings 嵌入模型使用 LangChain。有关 MistralAIEmbeddings 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
MistralAIEmbeddings	@langchain/mistralai	❌	✅

设置

要访问 MistralAI 嵌入模型，您需要创建一个 MistralAI 帐户，获取 API 密钥并安装 @langchain/mistralai 集成包。

凭据

前往 console.mistral.ai 注册 MistralAI 并生成 API 密钥。完成此操作后，设置 MISTRAL_API_KEY 环境变量

export MISTRAL_API_KEY="your-api-key"

如果您想获得对模型调用的自动跟踪，您还可以通过取消下面注释来设置您的 LangSmith API 密钥

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

安装

LangChain MistralAIEmbeddings 集成位于 @langchain/mistralai 包中

提示

请参阅本节以获取有关安装集成包的一般说明。

npm
yarn
pnpm

npm i @langchain/mistralai @langchain/core

yarn add @langchain/mistralai @langchain/core

pnpm add @langchain/mistralai @langchain/core

实例化

现在我们可以实例化我们的模型对象并生成聊天完成

import { MistralAIEmbeddings } from "@langchain/mistralai";

const embeddings = new MistralAIEmbeddings({
  model: "mistral-embed", // Default value
});

索引和检索

嵌入模型通常用于检索增强生成 (RAG) 流，既作为索引数据的一部分，也作为以后检索数据的一部分。有关更详细的说明，请参阅我们在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分别调用 embeddings.embedDocument(...) 和 embeddings.embedQuery(...) 为 fromDocuments 中使用的文本以及检索器的 invoke 操作中使用的文本创建嵌入。

您可以直接调用这些方法以获取嵌入以用于您自己的用例。

嵌入单个文本

您可以使用 embedQuery 嵌入查询以进行搜索。这会生成特定于查询的向量表示。

const singleVector = await embeddings.embedQuery(text);

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

[
          -0.04443359375,         0.01885986328125,      0.018035888671875,
    -0.00864410400390625,        0.049652099609375,     -0.001190185546875,
       0.028900146484375,       -0.035675048828125,   -0.00702667236328125,
  0.00016105175018310547,          -0.027587890625,      0.029388427734375,
      -0.053253173828125,   -0.0003020763397216797,     -0.046112060546875,
      0.0258026123046875,   -0.0010776519775390625,       0.02703857421875,
       0.040985107421875,       -0.004547119140625,     -0.020172119140625,
       -0.02606201171875,     -0.01457977294921875,          0.01220703125,
     -0.0078582763671875,         -0.0084228515625,      -0.02056884765625,
         -0.071044921875,         -0.0404052734375,    0.00923919677734375,
     0.01407623291015625,      -0.0210113525390625,  0.0006284713745117188,
    -0.01465606689453125,       0.0186309814453125,     -0.015838623046875,
   0.0007920265197753906,        -0.04437255859375,      0.008758544921875,
        -0.0172119140625,         0.01312255859375,   -0.01358795166015625,
     -0.0212860107421875, -0.000035822391510009766,    -0.0226898193359375,
    -0.01390838623046875,       -0.007659912109375,     -0.016021728515625,
       0.025909423828125,       -0.034515380859375,       -0.0372314453125,
       0.020355224609375,        -0.02606201171875,    -0.0158843994140625,
      -0.037994384765625,      0.00450897216796875,        0.0142822265625,
      -0.012725830078125,         -0.0770263671875,       0.02630615234375,
      -0.048614501953125,        0.006072998046875,    0.00417327880859375,
   -0.005138397216796875,         0.02557373046875,        0.0311279296875,
       0.026519775390625,      -0.0103607177734375,    -0.0108489990234375,
      -0.029510498046875,        0.022186279296875,     0.0256500244140625,
     -0.0186309814453125,          0.0443115234375,    -0.0304107666015625,
       -0.03131103515625,     0.007427215576171875,     0.0234527587890625,
      0.0224761962890625,      0.00463104248046875, -0.0037021636962890625,
      0.0302581787109375,          0.0733642578125,    -0.0121612548828125,
     -0.0172576904296875,        0.019317626953125,         0.029052734375,
     -0.0024871826171875,       0.0174713134765625,      0.026092529296875,
        0.04425048828125,   -0.0004563331604003906,     0.0146026611328125,
    -0.00748443603515625,         0.06146240234375,          0.02294921875,
         -0.016845703125,   -0.0014057159423828125,   -0.01435089111328125,
        0.06097412109375
]

嵌入多个文本

您可以使用 embedDocuments 嵌入多个文本以进行索引。用于此方法的内部机制可能（但不一定）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));

[
          -0.04443359375,         0.01885986328125,    0.0180511474609375,
     -0.0086517333984375,        0.049652099609375,  -0.00121307373046875,
      0.0289154052734375,        -0.03570556640625, -0.007015228271484375,
   0.0001499652862548828,      -0.0276641845703125,    0.0294036865234375,
          -0.05322265625,   -0.0002808570861816406,     -0.04608154296875,
        0.02581787109375,   -0.0011072158813476562,        0.027099609375,
       0.040985107421875,       -0.004547119140625,   -0.0201873779296875,
     -0.0260772705078125,      -0.0146026611328125,    0.0121917724609375,
      -0.007843017578125,      -0.0084381103515625,   -0.0205535888671875,
       -0.07110595703125,        -0.04046630859375,   0.00931549072265625,
        0.01409912109375,           -0.02099609375, 0.0006232261657714844,
      -0.014678955078125,       0.0186614990234375,   -0.0158233642578125,
    0.000812530517578125,        -0.04437255859375,   0.00873565673828125,
        -0.0172119140625,        0.013092041015625,      -0.0135498046875,
     -0.0212860107421875, -0.000006735324859619141,   -0.0226898193359375,
    -0.01389312744140625,      -0.0076751708984375,   -0.0160064697265625,
      0.0259246826171875,         -0.0345458984375,    -0.037200927734375,
       0.020355224609375,         -0.0260009765625,   -0.0159149169921875,
       -0.03802490234375,     0.004489898681640625,    0.0143280029296875,
    -0.01274871826171875,        -0.07708740234375,    0.0263214111328125,
       -0.04864501953125,      0.00608062744140625,  0.004192352294921875,
   -0.005115509033203125,       0.0255889892578125,       0.0311279296875,
      0.0265045166015625,      -0.0103607177734375,  -0.01084136962890625,
     -0.0294952392578125,        0.022186279296875,    0.0256500244140625,
        -0.0186767578125,        0.044342041015625,    -0.030426025390625,
       -0.03131103515625,     0.007396697998046875,    0.0234527587890625,
            0.0224609375,     0.004634857177734375, -0.003643035888671875,
      0.0302886962890625,         0.07342529296875,  -0.01221466064453125,
      -0.017303466796875,       0.0193023681640625,        0.029052734375,
  -0.0024890899658203125,       0.0174407958984375,        0.026123046875,
       0.044219970703125,   -0.0004944801330566406,   0.01462554931640625,
   -0.007450103759765625,         0.06146240234375,     0.022979736328125,
         -0.016845703125,    -0.001445770263671875,   -0.0143890380859375,
        0.06097412109375
]
[
       -0.02032470703125,       0.02606201171875,      0.051605224609375,
        -0.0281982421875,      0.055755615234375,   0.001987457275390625,
          0.031982421875,    -0.0131378173828125,       -0.0252685546875,
    0.001010894775390625,     -0.024017333984375,      0.053375244140625,
      -0.042816162109375,      0.005584716796875,      -0.04132080078125,
        0.03021240234375,       0.01324462890625,      0.016876220703125,
       0.041961669921875,  -0.004299163818359375,    -0.0273895263671875,
      -0.039642333984375,     -0.021575927734375,     0.0309295654296875,
     -0.0099945068359375,    -0.0163726806640625,   -0.00968170166015625,
       -0.07733154296875,     -0.030364990234375,  -0.003864288330078125,
       0.016387939453125,       -0.0389404296875,    -0.0026702880859375,
     -0.0176544189453125,     0.0264434814453125,      -0.01226806640625,
  -0.0022220611572265625,     -0.039703369140625,   -0.00907135009765625,
     -0.0260467529296875,       0.03155517578125, -0.0004324913024902344,
      -0.019500732421875,    -0.0120697021484375,        -0.008544921875,
       -0.01654052734375,       0.00067138671875,    -0.0134735107421875,
        0.01080322265625,     -0.034759521484375,         -0.06201171875,
       0.012359619140625,  -0.006237030029296875,    -0.0168914794921875,
     -0.0183563232421875,     0.0236053466796875, -0.0021419525146484375,
     -0.0164947509765625,     -0.052581787109375,      0.022125244140625,
      -0.045745849609375, -0.0009088516235351562,     0.0097808837890625,
  -0.0009326934814453125,      0.041656494140625,        0.0269775390625,
          0.016845703125, -0.0022335052490234375,    -0.0182342529296875,
     -0.0245208740234375,  0.0036602020263671875,    -0.0188751220703125,
     -0.0023956298828125,     0.0238800048828125,     -0.034942626953125,
      -0.033782958984375,     0.0046234130859375,        0.0318603515625,
      0.0251007080078125, -0.0023288726806640625,    -0.0225677490234375,
   0.0004394054412841797,         0.064208984375,    -0.0254669189453125,
     -0.0234222412109375,  0.0009264945983886719,    0.01464080810546875,
    0.006626129150390625,  -0.007450103759765625,       0.02642822265625,
         0.0260009765625,    0.00536346435546875,    0.01479339599609375,
  -0.0032253265380859375,           0.0498046875,      0.048248291015625,
    -0.01519012451171875,    0.00605010986328125,      0.019744873046875,
      0.0296478271484375
]

API 参考

有关所有 MistralAIEmbeddings 功能和配置的详细文档，请前往 API 参考：https://api.js.langchain.com/classes/langchain_mistralai.MistralAIEmbeddings.html

嵌入模型概念指南
嵌入模型操作指南

MistralAIEmbeddings

概述

集成详细信息

设置

凭据

安装

实例化

索引和检索

直接使用

嵌入单个文本

嵌入多个文本

API 参考

此页面是否有帮助？

您也可以留下详细的反馈在 GitHub 上.

概述​

集成详细信息​

设置​

凭据​

安装​

实例化​

索引和检索​

直接使用​

嵌入单个文本​

嵌入多个文本​

API 参考​

相关​

此页面是否有帮助？

您也可以留下详细的反馈 在 GitHub 上.

概述

集成详细信息

设置

凭据

安装

实例化

索引和检索

直接使用

嵌入单个文本

嵌入多个文本

API 参考

相关

您也可以留下详细的反馈在 GitHub 上.