> ## Documentation Index > Fetch the complete documentation index at: https://docs.langchain.com/llms.txt > Use this file to discover all available pages before exploring further. # OllamaEmbeddings integration > Integrate with the OllamaEmbeddings embedding model using LangChain JavaScript. This will help you get started with Ollama [embedding models](/oss/javascript/integrations/embeddings) using LangChain. For detailed documentation on `OllamaEmbeddings` features and configuration options, please refer to the [API reference](https://reference.langchain.com/javascript/langchain-ollama/OllamaEmbeddings). ## Overview ### Integration details | Class | Package | Local | [Py support](https://python.langchain.com/docs/integrations/embeddings/ollama/) | Downloads | Version | | :------------------------------------------------------------------------------------------------- | :--------------------------------------------------------- | :---: | :-----------------------------------------------------------------------------: | :-----------------------------------------------------------------------------------------------: | :--------------------------------------------------------------------------------------------: | | [`OllamaEmbeddings`](https://reference.langchain.com/javascript/langchain-ollama/OllamaEmbeddings) | [`@langchain/ollama`](https://npmjs.com/@langchain/ollama) | ✅ | ✅ | ![NPM - Downloads](https://img.shields.io/npm/dm/@langchain/ollama?style=flat-square\&label=%20&) | ![NPM - Version](https://img.shields.io/npm/v/@langchain/ollama?style=flat-square\&label=%20&) | ## Setup To access Ollama embedding models, follow [these instructions](https://github.com/ollama/ollama) to install Ollama, then install the `@langchain/ollama` integration package. ### Credentials If you want to get automated tracing of your model calls you can also set your [LangSmith](/langsmith/observability) API key by uncommenting below: ```bash theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} # export LANGSMITH_TRACING="true" # export LANGSMITH_API_KEY="your-api-key" ``` ### Installation The LangChain OllamaEmbeddings integration lives in the `@langchain/ollama` package: ```bash npm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} npm install @langchain/ollama @langchain/core ``` ```bash yarn theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} yarn add @langchain/ollama @langchain/core ``` ```bash pnpm theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} pnpm add @langchain/ollama @langchain/core ``` ## Instantiation Now we can instantiate our model object and embed text: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} import { OllamaEmbeddings } from "@langchain/ollama"; const embeddings = new OllamaEmbeddings({ model: "mxbai-embed-large", // Default value baseUrl: "http://localhost:11434", // Default value }); ``` ## Indexing and retrieval Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our RAG tutorials under the [**Learn** tab](/oss/javascript/learn/). Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document using the demo [`MemoryVectorStore`](/oss/javascript/integrations/vectorstores/memory). ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} // 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; ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} LangChain is the framework for building context-aware reasoning applications ``` ## Direct usage Under the hood, the vectorstore and retriever implementations are calling `embeddings.embedDocument(...)` and `embeddings.embedQuery(...)` to create embeddings for the text(s) used in `fromDocuments` and the retriever's `invoke` operations, respectively. You can directly call these methods to get embeddings for your own use cases. ### Embed single texts You can embed queries for search with `embedQuery`. This generates a vector representation specific to the query: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} const singleVector = await embeddings.embedQuery(text); console.log(singleVector.slice(0, 100)); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ 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 ] ``` ### Embed multiple texts You can embed multiple texts for indexing with `embedDocuments`. The internals used for this method may (but do not have to) differ from embedding queries: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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)); ``` ```text theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} [ 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 [model parameters](https://github.com/jmorganca/ollama/blob/main/docs/modelfile.md#valid-parameters-and-values) are also supported: ```typescript theme={"theme":{"light":"catppuccin-latte","dark":"catppuccin-mocha"}} 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 }, }); ``` ## Related * Embedding model [conceptual guide](/oss/javascript/integrations/embeddings) * Embedding model [how-to guides](/oss/javascript/integrations/embeddings) *** ## API reference For detailed documentation of all `OllamaEmbeddings` features and configurations head to the [API reference](https://reference.langchain.com/javascript/langchain-ollama/OllamaEmbeddings) ***
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