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*   [Modules](/v0.1/docs/modules/)
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*   [Retrievers](/v0.1/docs/modules/data_connection/retrievers/)
*   Matryoshka Retriever

On this page

Matryoshka Retriever
====================

This is an implementation of the [Supabase](https://supabase.com/) blog post ["Matryoshka embeddings: faster OpenAI vector search using Adaptive Retrieval"](https://supabase.com/blog/matryoshka-embeddings).

![Matryoshka Retriever](/v0.1/assets/images/adaptive_retrieval-2abb9f6f280c11a424ae6978d39eb011.png)

### Overview[​](#overview "Direct link to Overview")

This class performs "Adaptive Retrieval" for searching text embeddings efficiently using the Matryoshka Representation Learning (MRL) technique. It retrieves documents similar to a query embedding in two steps:

*   **First-pass**: Uses a lower dimensional sub-vector from the MRL embedding for an initial, fast, but less accurate search.
    
*   **Second-pass**: Re-ranks the top results from the first pass using the full, high-dimensional embedding for higher accuracy.
    

This code demonstrates using MRL embeddings for efficient vector search by combining faster, lower-dimensional initial search with accurate, high-dimensional re-ranking.

Example[​](#example "Direct link to Example")
---------------------------------------------

### Setup[​](#setup "Direct link to Setup")

tip

See [this section for general instructions on installing integration packages](/v0.1/docs/get_started/installation/#installing-integration-packages).

*   npm
*   Yarn
*   pnpm

    npm install @langchain/openai @langchain/community

    yarn add @langchain/openai @langchain/community

    pnpm add @langchain/openai @langchain/community

To follow the example below, you need an OpenAI API key:

    export OPENAI_API_KEY=your-api-key

We'll also be using `chroma` for our vector store. Follow the instructions [here](/v0.1/docs/integrations/vectorstores/chroma/) to setup.

    import { MatryoshkaRetriever } from "langchain/retrievers/matryoshka_retriever";import { Chroma } from "@langchain/community/vectorstores/chroma";import { OpenAIEmbeddings } from "@langchain/openai";import { Document } from "@langchain/core/documents";import { faker } from "@faker-js/faker";const smallEmbeddings = new OpenAIEmbeddings({  model: "text-embedding-3-small",  dimensions: 512, // Min num for small});const largeEmbeddings = new OpenAIEmbeddings({  model: "text-embedding-3-large",  dimensions: 3072, // Max num for large});const vectorStore = new Chroma(smallEmbeddings, {  numDimensions: 512,});const retriever = new MatryoshkaRetriever({  vectorStore,  largeEmbeddingModel: largeEmbeddings,  largeK: 5,});const irrelevantDocs = Array.from({ length: 250 }).map(  () =>    new Document({      pageContent: faker.lorem.word(7), // Similar length to the relevant docs    }));const relevantDocs = [  new Document({    pageContent: "LangChain is an open source github repo",  }),  new Document({    pageContent: "There are JS and PY versions of the LangChain github repos",  }),  new Document({    pageContent: "LangGraph is a new open source library by the LangChain team",  }),  new Document({    pageContent: "LangChain announced GA of LangSmith last week!",  }),  new Document({    pageContent: "I heart LangChain",  }),];const allDocs = [...irrelevantDocs, ...relevantDocs];/** * IMPORTANT: * The `addDocuments` method on `MatryoshkaRetriever` will * generate the small AND large embeddings for all documents. */await retriever.addDocuments(allDocs);const query = "What is LangChain?";const results = await retriever.invoke(query);console.log(results.map(({ pageContent }) => pageContent).join("\n"));/**I heart LangChainLangGraph is a new open source library by the LangChain teamLangChain is an open source github repoLangChain announced GA of LangSmith last week!There are JS and PY versions of the LangChain github repos */

#### API Reference:

*   [MatryoshkaRetriever](https://api.js.langchain.com/classes/langchain_retrievers_matryoshka_retriever.MatryoshkaRetriever.html) from `langchain/retrievers/matryoshka_retriever`
*   [Chroma](https://api.js.langchain.com/classes/langchain_community_vectorstores_chroma.Chroma.html) from `@langchain/community/vectorstores/chroma`
*   [OpenAIEmbeddings](https://api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [Document](https://api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

note

Due to the constraints of some vector stores, the large embedding metadata field is stringified (`JSON.stringify`) before being stored. This means that the metadata field will need to be parsed (`JSON.parse`) when retrieved from the vector store.

* * *

#### Help us out by providing feedback on this documentation page:

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MultiQuery Retriever

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*   [Overview](#overview)
*   [Example](#example)
    *   [Setup](#setup)

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