跳到主要内容
这能帮助您开始使用 LangChain 的 Ollama 嵌入模型。有关 OllamaEmbeddings 功能和配置选项的详细文档，请参阅 API 参考。

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概览

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集成详情

类别包本地Python 支持下载量版本
OllamaEmbeddings@langchain/ollama✅✅NPM - DownloadsNPM - Version

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设置

要访问 Ollama 嵌入模型，您需要按照这些说明安装 Ollama，并安装 @langchain/ollama 集成包。

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凭据

如果您想获取模型调用的自动化跟踪，您还可以通过取消注释下方来设置您的 LangSmith API 密钥 
# export LANGSMITH_TRACING="true"
# export LANGSMITH_API_KEY="your-api-key"

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安装

LangChain OllamaEmbeddings 集成位于 @langchain/ollama 包中 
npm install @langchain/ollama @langchain/core

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实例化

现在我们可以实例化模型对象并嵌入文本 
import { OllamaEmbeddings } from "@langchain/ollama";

const embeddings = new OllamaEmbeddings({
  model: "mxbai-embed-large", // Default value
  baseUrl: "https://:11434", // Default value
});

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索引和检索

嵌入模型通常用于检索增强生成 (RAG) 流程中，既作为索引数据的一部分，也用于后续检索数据。有关更详细的说明，请参阅学习选项卡下的 RAG 教程。 下面，我们将演示如何使用我们上面初始化的 embeddings 对象来索引和检索数据。在此示例中，我们将使用演示 MemoryVectorStore 索引和检索一个示例文档。
// Create a vector store with a sample text
import { MemoryVectorStore } from "@langchain/classic/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

在底层，向量存储和检索器实现分别调用 embeddings.embedDocument(...) 和 embeddings.embedQuery(...) 来为 fromDocuments 和检索器的 invoke 操作中使用的文本创建嵌入。 您可以直接调用这些方法来获取您自己用例的嵌入。

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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));

[
   0.026051683,   0.029081265,  -0.040726297,  -0.015116953, -0.010691089,
   0.030181013, -0.0065084146,   -0.02079503,   0.013575795,   0.03452527,
   0.009578291,   0.007026421,  -0.030110886,   0.013489622,  -0.04294787,
   0.011141899,  -0.043768786,   -0.00362867, -0.0081198225,  -0.03426076,
   0.010075142,   0.027787417,   -0.09052663,   -0.06039698, -0.009462592,
    0.06232288,   0.051121354,   0.011977532,   0.089046724,  0.059000008,
   0.031860664,  -0.034242127,   0.020339863,   0.011483523,  -0.05429335,
   -0.04963588,    0.03263794,   -0.05581542,   0.013908403, -0.012356067,
  -0.007802118,  -0.010027855,    0.00281217,  -0.101886116, -0.079341754,
   0.011269771,  0.0035983133,  -0.027667878,   0.032092705, -0.052843474,
  -0.045283325,     0.0382421,     0.0193055,   0.011050924,  0.021132186,
  -0.037696265,  0.0006107435,  0.0043520257,  -0.028798066,  0.049155913,
    0.03590549, -0.0040995986,   0.019772101,  -0.076119535, 0.0031298609,
    0.03368174,   0.039398745,  -0.011813277,  -0.019313531, -0.013108803,
  -0.044905286,  -0.022326004,   -0.01656178,   -0.06658457,  0.016789088,
   0.049952697,   0.006615693,   -0.01694402,  -0.018105473, 0.0049101883,
  -0.004966945,   0.049762275,   -0.03556957,  -0.015986584,  -0.03190983,
   -0.05336687, -0.0020468342, -0.0016106658,  -0.035291273, -0.029783724,
  -0.010153295,   0.052100364,    0.05528949,    0.01379487, -0.024542747,
   0.028773975,   0.010087022,   0.030448131,  -0.042391222,  0.016596776
]

​
嵌入多个文本

您可以使用 embedDocuments 嵌入多个文本以进行索引。此方法使用的内部机制可能（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));

[
   0.026051683,   0.029081265,  -0.040726297,  -0.015116953, -0.010691089,
   0.030181013, -0.0065084146,   -0.02079503,   0.013575795,   0.03452527,
   0.009578291,   0.007026421,  -0.030110886,   0.013489622,  -0.04294787,
   0.011141899,  -0.043768786,   -0.00362867, -0.0081198225,  -0.03426076,
   0.010075142,   0.027787417,   -0.09052663,   -0.06039698, -0.009462592,
    0.06232288,   0.051121354,   0.011977532,   0.089046724,  0.059000008,
   0.031860664,  -0.034242127,   0.020339863,   0.011483523,  -0.05429335,
   -0.04963588,    0.03263794,   -0.05581542,   0.013908403, -0.012356067,
  -0.007802118,  -0.010027855,    0.00281217,  -0.101886116, -0.079341754,
   0.011269771,  0.0035983133,  -0.027667878,   0.032092705, -0.052843474,
  -0.045283325,     0.0382421,     0.0193055,   0.011050924,  0.021132186,
  -0.037696265,  0.0006107435,  0.0043520257,  -0.028798066,  0.049155913,
    0.03590549, -0.0040995986,   0.019772101,  -0.076119535, 0.0031298609,
    0.03368174,   0.039398745,  -0.011813277,  -0.019313531, -0.013108803,
  -0.044905286,  -0.022326004,   -0.01656178,   -0.06658457,  0.016789088,
   0.049952697,   0.006615693,   -0.01694402,  -0.018105473, 0.0049101883,
  -0.004966945,   0.049762275,   -0.03556957,  -0.015986584,  -0.03190983,
   -0.05336687, -0.0020468342, -0.0016106658,  -0.035291273, -0.029783724,
  -0.010153295,   0.052100364,    0.05528949,    0.01379487, -0.024542747,
   0.028773975,   0.010087022,   0.030448131,  -0.042391222,  0.016596776
]
[
      0.0558515,   0.028698817,  -0.037476595,  0.0048659276,  -0.019229038,
    -0.04713716,  -0.020947812,  -0.017550547,    0.01205507,   0.027693441,
   -0.011791304,   0.009862203,   0.019662278,  -0.037511427,  -0.022662448,
    0.036224432,  -0.051760387,  -0.030165697,  -0.008899774,  -0.024518963,
    0.010077767,   0.032209765,    -0.0854303,  -0.038666975,  -0.036021013,
    0.060899545,   0.045867186,   0.003365381,    0.09387081,   0.038216405,
    0.011449426,  -0.016495887,   0.020602569,   -0.02368503,  -0.014733645,
   -0.065408126, -0.0065152845,  -0.027103946, 0.00038956117,   -0.08648814,
    0.029316466,  -0.054449145,   0.034129277,  -0.055225655,  -0.043182302,
   0.0011148591,   0.044116337,  -0.046552557,   0.032423045,   -0.03269365,
    -0.05062933,   0.021473562,  -0.011019348,  -0.019621233, -0.0003149565,
  -0.0046085776,  0.0052610254, -0.0029293327,  -0.035793293,   0.034469575,
    0.037724957,   0.009572597,   0.014198464,    -0.0878237,  0.0056973165,
    0.023563445,   0.030928325,   0.025520306,    0.01836824,  -0.016456697,
   -0.061934732,   0.009764942,  -0.035812028,   -0.04429064,   0.031323086,
    0.056027107, -0.0019782048,  -0.015204176,  -0.008684945, -0.0010460864,
    0.054642987,   0.044149086,  -0.032964867,  -0.012044753,  -0.019075096,
   -0.027932597,   0.018542245,   -0.02602878,   -0.04645578,  -0.020976603,
    0.018999187,   0.050663687,   0.016725155,  0.0076955976,   0.011448177,
    0.053931057,   -0.03234989,   0.024429373,  -0.023123834,    0.02197912
]
Ollama 模型参数也受支持 
import { OllamaEmbeddings } from "@langchain/ollama";

const embeddingsCustomParams = new OllamaEmbeddings({
  requestOptions: {
    useMmap: true, // use_mmap 1
    numThread: 6, // num_thread 6
    numGpu: 1, // num_gpu 1
  },
});

​
API 参考

有关所有 OllamaEmbeddings 功能和配置的详细文档，请前往 API 参考
在 GitHub 上编辑此页面源文件。
以编程方式连接这些文档到 Claude、VSCode 等，通过 MCP 获取实时答案。
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