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*   [Introduction](/v0.2/docs/introduction)
*   [Tutorials](/v0.2/docs/tutorials/)
    
    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
    *   [Tutorials](/v0.2/docs/tutorials/)
    *   [Build a Simple LLM Application](/v0.2/docs/tutorials/llm_chain)
    *   [Build a Query Analysis System](/v0.2/docs/tutorials/query_analysis)
    *   [Build a Chatbot](/v0.2/docs/tutorials/chatbot)
    *   [Build an Agent](/v0.2/docs/tutorials/agents)
    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
    *   [Tagging](/v0.2/docs/tutorials/classification)
    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
    *   [Conversational RAG](/v0.2/docs/tutorials/qa_chat_history)
    *   [Build a Retrieval Augmented Generation (RAG) App](/v0.2/docs/tutorials/rag)
    *   [Build a Question/Answering system over SQL data](/v0.2/docs/tutorials/sql_qa)
*   [How-to guides](/v0.2/docs/how_to/)
    
    *   [How-to guides](/v0.2/docs/how_to/)
    *   [How to use example selectors](/v0.2/docs/how_to/example_selectors)
    *   [Installation](/v0.2/docs/how_to/installation)
    *   [How to stream responses from an LLM](/v0.2/docs/how_to/streaming_llm)
    *   [How to stream chat model responses](/v0.2/docs/how_to/chat_streaming)
    *   [How to embed text data](/v0.2/docs/how_to/embed_text)
    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
    *   [How to cache model responses](/v0.2/docs/how_to/llm_caching)
    *   [How to cache chat model responses](/v0.2/docs/how_to/chat_model_caching)
    *   [How to create a custom LLM class](/v0.2/docs/how_to/custom_llm)
    *   [How to use few shot examples](/v0.2/docs/how_to/few_shot_examples)
    *   [How to use output parsers to parse an LLM response into structured format](/v0.2/docs/how_to/output_parser_structured)
    *   [How to return structured data from a model](/v0.2/docs/how_to/structured_output)
    *   [How to add ad-hoc tool calling capability to LLMs and Chat Models](/v0.2/docs/how_to/tools_prompting)
    *   [How to create a custom chat model class](/v0.2/docs/how_to/custom_chat)
    *   [How to do per-user retrieval](/v0.2/docs/how_to/qa_per_user)
    *   [How to track token usage](/v0.2/docs/how_to/chat_token_usage_tracking)
    *   [How to track token usage](/v0.2/docs/how_to/llm_token_usage_tracking)
    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
    *   [How to compose prompts together](/v0.2/docs/how_to/prompts_composition)
    *   [How to use legacy LangChain Agents (AgentExecutor)](/v0.2/docs/how_to/agent_executor)
    *   [How to add values to a chain's state](/v0.2/docs/how_to/assign)
    *   [How to attach runtime arguments to a Runnable](/v0.2/docs/how_to/binding)
    *   [How to cache embedding results](/v0.2/docs/how_to/caching_embeddings)
    *   [How to split by character](/v0.2/docs/how_to/character_text_splitter)
    *   [How to manage memory](/v0.2/docs/how_to/chatbots_memory)
    *   [How to do retrieval](/v0.2/docs/how_to/chatbots_retrieval)
    *   [How to use tools](/v0.2/docs/how_to/chatbots_tools)
    *   [How to split code](/v0.2/docs/how_to/code_splitter)
    *   [How to do retrieval with contextual compression](/v0.2/docs/how_to/contextual_compression)
    *   [How to write a custom retriever class](/v0.2/docs/how_to/custom_retriever)
    *   [How to create custom Tools](/v0.2/docs/how_to/custom_tools)
    *   [How to debug your LLM apps](/v0.2/docs/how_to/debugging)
    *   [How to load CSV data](/v0.2/docs/how_to/document_loader_csv)
    *   [How to write a custom document loader](/v0.2/docs/how_to/document_loader_custom)
    *   [How to load data from a directory](/v0.2/docs/how_to/document_loader_directory)
    *   [How to load PDF files](/v0.2/docs/how_to/document_loader_pdf)
    *   [How to load JSON data](/v0.2/docs/how_to/document_loaders_json)
    *   [How to select examples by length](/v0.2/docs/how_to/example_selectors_length_based)
    *   [How to select examples by similarity](/v0.2/docs/how_to/example_selectors_similarity)
    *   [How to use reference examples](/v0.2/docs/how_to/extraction_examples)
    *   [How to handle long text](/v0.2/docs/how_to/extraction_long_text)
    *   [How to do extraction without using function calling](/v0.2/docs/how_to/extraction_parse)
    *   [Fallbacks](/v0.2/docs/how_to/fallbacks)
    *   [Few Shot Prompt Templates](/v0.2/docs/how_to/few_shot)
    *   [How to run custom functions](/v0.2/docs/how_to/functions)
    *   [How to construct knowledge graphs](/v0.2/docs/how_to/graph_constructing)
    *   [How to map values to a database](/v0.2/docs/how_to/graph_mapping)
    *   [How to improve results with prompting](/v0.2/docs/how_to/graph_prompting)
    *   [How to add a semantic layer over the database](/v0.2/docs/how_to/graph_semantic)
    *   [How to reindex data to keep your vectorstore in-sync with the underlying data source](/v0.2/docs/how_to/indexing)
    *   [How to get log probabilities](/v0.2/docs/how_to/logprobs)
    *   [How to add message history](/v0.2/docs/how_to/message_history)
    *   [How to generate multiple embeddings per document](/v0.2/docs/how_to/multi_vector)
    *   [How to generate multiple queries to retrieve data for](/v0.2/docs/how_to/multiple_queries)
    *   [How to parse JSON output](/v0.2/docs/how_to/output_parser_json)
    *   [How to retry when output parsing errors occur](/v0.2/docs/how_to/output_parser_retry)
    *   [How to parse XML output](/v0.2/docs/how_to/output_parser_xml)
    *   [How to invoke runnables in parallel](/v0.2/docs/how_to/parallel)
    *   [How to retrieve the whole document for a chunk](/v0.2/docs/how_to/parent_document_retriever)
    *   [How to partially format prompt templates](/v0.2/docs/how_to/prompts_partial)
    *   [How to add chat history to a question-answering chain](/v0.2/docs/how_to/qa_chat_history_how_to)
    *   [How to return citations](/v0.2/docs/how_to/qa_citations)
    *   [How to return sources](/v0.2/docs/how_to/qa_sources)
    *   [How to stream from a question-answering chain](/v0.2/docs/how_to/qa_streaming)
    *   [How to construct filters](/v0.2/docs/how_to/query_constructing_filters)
    *   [How to add examples to the prompt](/v0.2/docs/how_to/query_few_shot)
    *   [How to deal with high cardinality categorical variables](/v0.2/docs/how_to/query_high_cardinality)
    *   [How to handle multiple queries](/v0.2/docs/how_to/query_multiple_queries)
    *   [How to handle multiple retrievers](/v0.2/docs/how_to/query_multiple_retrievers)
    *   [How to handle cases where no queries are generated](/v0.2/docs/how_to/query_no_queries)
    *   [How to recursively split text by characters](/v0.2/docs/how_to/recursive_text_splitter)
    *   [How to reduce retrieval latency](/v0.2/docs/how_to/reduce_retrieval_latency)
    *   [How to route execution within a chain](/v0.2/docs/how_to/routing)
    *   [How to chain runnables](/v0.2/docs/how_to/sequence)
    *   [How to split text by tokens](/v0.2/docs/how_to/split_by_token)
    *   [How to deal with large databases](/v0.2/docs/how_to/sql_large_db)
    *   [How to use prompting to improve results](/v0.2/docs/how_to/sql_prompting)
    *   [How to do query validation](/v0.2/docs/how_to/sql_query_checking)
    *   [How to stream](/v0.2/docs/how_to/streaming)
    *   [How to create a time-weighted retriever](/v0.2/docs/how_to/time_weighted_vectorstore)
    *   [How to use a chat model to call tools](/v0.2/docs/how_to/tool_calling)
    *   [How to call tools with multi-modal data](/v0.2/docs/how_to/tool_calls_multi_modal)
    *   [How to use LangChain tools](/v0.2/docs/how_to/tools_builtin)
    *   [How use a vector store to retrieve data](/v0.2/docs/how_to/vectorstore_retriever)
    *   [How to create and query vector stores](/v0.2/docs/how_to/vectorstores)
*   [Conceptual guide](/v0.2/docs/concepts)
*   Ecosystem
    
    *   [🦜🛠️ LangSmith](/v0.2/docs/langsmith/)
        
    *   [🦜🕸️LangGraph.js](/v0.2/docs/langgraph)
*   Versions
    
    *   [Overview](/v0.2/docs/versions/overview)
    *   [v0.2](/v0.2/docs/versions/v0_2)
    *   [Release Policy](/v0.2/docs/versions/release_policy)
    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   How to generate multiple embeddings per document

On this page

How to generate multiple embeddings per document
================================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Retrievers](/v0.2/docs/concepts/#retrievers)
*   [Text splitters](/v0.2/docs/concepts/#text-splitters)
*   [Retrieval-augmented generation (RAG)](/v0.2/docs/tutorials/rag)

Embedding different representations of an original document, then returning the original document when any of the representations result in a search hit, can allow you to tune and improve your retrieval performance. LangChain has a base [`MultiVectorRetriever`](https://v02.api.js.langchain.com/classes/langchain_retrievers_multi_vector.MultiVectorRetriever.html) designed to do just this!

A lot of the complexity lies in how to create the multiple vectors per document. This guide covers some of the common ways to create those vectors and use the `MultiVectorRetriever`.

Some methods to create multiple vectors per document include:

*   smaller chunks: split a document into smaller chunks, and embed those (e.g. the [`ParentDocumentRetriever`](/v0.2/docs/how_to/parent_document_retriever))
*   summary: create a summary for each document, embed that along with (or instead of) the document
*   hypothetical questions: create hypothetical questions that each document would be appropriate to answer, embed those along with (or instead of) the document

Note that this also enables another method of adding embeddings - manually. This is great because you can explicitly add questions or queries that should lead to a document being recovered, giving you more control.

Smaller chunks[​](#smaller-chunks "Direct link to Smaller chunks")
------------------------------------------------------------------

Often times it can be useful to retrieve larger chunks of information, but embed smaller chunks. This allows for embeddings to capture the semantic meaning as closely as possible, but for as much context as possible to be passed downstream. NOTE: this is what the ParentDocumentRetriever does. Here we show what is going on under the hood.

tip

See [this section for general instructions on installing integration packages](/v0.2/docs/how_to/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

    import * as uuid from "uuid";import { MultiVectorRetriever } from "langchain/retrievers/multi_vector";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { OpenAIEmbeddings } from "@langchain/openai";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { InMemoryStore } from "@langchain/core/stores";import { TextLoader } from "langchain/document_loaders/fs/text";import { Document } from "@langchain/core/documents";const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 10000,  chunkOverlap: 20,});const docs = await splitter.splitDocuments(parentDocuments);const idKey = "doc_id";const docIds = docs.map((_) => uuid.v4());const childSplitter = new RecursiveCharacterTextSplitter({  chunkSize: 400,  chunkOverlap: 0,});const subDocs = [];for (let i = 0; i < docs.length; i += 1) {  const childDocs = await childSplitter.splitDocuments([docs[i]]);  const taggedChildDocs = childDocs.map((childDoc) => {    // eslint-disable-next-line no-param-reassign    childDoc.metadata[idKey] = docIds[i];    return childDoc;  });  subDocs.push(...taggedChildDocs);}// The byteStore to use to store the original chunksconst byteStore = new InMemoryStore<Uint8Array>();// The vectorstore to use to index the child chunksconst vectorstore = await FaissStore.fromDocuments(  subDocs,  new OpenAIEmbeddings());const retriever = new MultiVectorRetriever({  vectorstore,  byteStore,  idKey,  // Optional `k` parameter to search for more child documents in VectorStore.  // Note that this does not exactly correspond to the number of final (parent) documents  // retrieved, as multiple child documents can point to the same parent.  childK: 20,  // Optional `k` parameter to limit number of final, parent documents returned from this  // retriever and sent to LLM. This is an upper-bound, and the final count may be lower than this.  parentK: 5,});const keyValuePairs: [string, Document][] = docs.map((originalDoc, i) => [  docIds[i],  originalDoc,]);// Use the retriever to add the original chunks to the document storeawait retriever.docstore.mset(keyValuePairs);// Vectorstore alone retrieves the small chunksconst vectorstoreResult = await retriever.vectorstore.similaritySearch(  "justice breyer");console.log(vectorstoreResult[0].pageContent.length);/*  390*/// Retriever returns larger resultconst retrieverResult = await retriever.invoke("justice breyer");console.log(retrieverResult[0].pageContent.length);/*  9770*/

#### API Reference:

*   [MultiVectorRetriever](https://v02.api.js.langchain.com/classes/langchain_retrievers_multi_vector.MultiVectorRetriever.html) from `langchain/retrievers/multi_vector`
*   [FaissStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [InMemoryStore](https://v02.api.js.langchain.com/classes/langchain_core_stores.InMemoryStore.html) from `@langchain/core/stores`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

Summary[​](#summary "Direct link to Summary")
---------------------------------------------

Oftentimes a summary may be able to distill more accurately what a chunk is about, leading to better retrieval. Here we show how to create summaries, and then embed those.

    import * as uuid from "uuid";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { MultiVectorRetriever } from "langchain/retrievers/multi_vector";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { InMemoryStore } from "@langchain/core/stores";import { TextLoader } from "langchain/document_loaders/fs/text";import { PromptTemplate } from "@langchain/core/prompts";import { StringOutputParser } from "@langchain/core/output_parsers";import { RunnableSequence } from "@langchain/core/runnables";import { Document } from "@langchain/core/documents";const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 10000,  chunkOverlap: 20,});const docs = await splitter.splitDocuments(parentDocuments);const chain = RunnableSequence.from([  { content: (doc: Document) => doc.pageContent },  PromptTemplate.fromTemplate(`Summarize the following document:\n\n{content}`),  new ChatOpenAI({    maxRetries: 0,  }),  new StringOutputParser(),]);const summaries = await chain.batch(docs, {  maxConcurrency: 5,});const idKey = "doc_id";const docIds = docs.map((_) => uuid.v4());const summaryDocs = summaries.map((summary, i) => {  const summaryDoc = new Document({    pageContent: summary,    metadata: {      [idKey]: docIds[i],    },  });  return summaryDoc;});// The byteStore to use to store the original chunksconst byteStore = new InMemoryStore<Uint8Array>();// The vectorstore to use to index the child chunksconst vectorstore = await FaissStore.fromDocuments(  summaryDocs,  new OpenAIEmbeddings());const retriever = new MultiVectorRetriever({  vectorstore,  byteStore,  idKey,});const keyValuePairs: [string, Document][] = docs.map((originalDoc, i) => [  docIds[i],  originalDoc,]);// Use the retriever to add the original chunks to the document storeawait retriever.docstore.mset(keyValuePairs);// We could also add the original chunks to the vectorstore if we wish// const taggedOriginalDocs = docs.map((doc, i) => {//   doc.metadata[idKey] = docIds[i];//   return doc;// });// retriever.vectorstore.addDocuments(taggedOriginalDocs);// Vectorstore alone retrieves the small chunksconst vectorstoreResult = await retriever.vectorstore.similaritySearch(  "justice breyer");console.log(vectorstoreResult[0].pageContent.length);/*  1118*/// Retriever returns larger resultconst retrieverResult = await retriever.invoke("justice breyer");console.log(retrieverResult[0].pageContent.length);/*  9770*/

#### API Reference:

*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [MultiVectorRetriever](https://v02.api.js.langchain.com/classes/langchain_retrievers_multi_vector.MultiVectorRetriever.html) from `langchain/retrievers/multi_vector`
*   [FaissStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [InMemoryStore](https://v02.api.js.langchain.com/classes/langchain_core_stores.InMemoryStore.html) from `@langchain/core/stores`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [StringOutputParser](https://v02.api.js.langchain.com/classes/langchain_core_output_parsers.StringOutputParser.html) from `@langchain/core/output_parsers`
*   [RunnableSequence](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`

Hypothetical queries[​](#hypothetical-queries "Direct link to Hypothetical queries")
------------------------------------------------------------------------------------

An LLM can also be used to generate a list of hypothetical questions that could be asked of a particular document. These questions can then be embedded and used to retrieve the original document:

    import * as uuid from "uuid";import { ChatOpenAI, OpenAIEmbeddings } from "@langchain/openai";import { MultiVectorRetriever } from "langchain/retrievers/multi_vector";import { FaissStore } from "@langchain/community/vectorstores/faiss";import { RecursiveCharacterTextSplitter } from "@langchain/textsplitters";import { InMemoryStore } from "@langchain/core/stores";import { TextLoader } from "langchain/document_loaders/fs/text";import { PromptTemplate } from "@langchain/core/prompts";import { RunnableSequence } from "@langchain/core/runnables";import { Document } from "@langchain/core/documents";import { JsonKeyOutputFunctionsParser } from "@langchain/core/output_parsers/openai_functions";const textLoader = new TextLoader("../examples/state_of_the_union.txt");const parentDocuments = await textLoader.load();const splitter = new RecursiveCharacterTextSplitter({  chunkSize: 10000,  chunkOverlap: 20,});const docs = await splitter.splitDocuments(parentDocuments);const functionsSchema = [  {    name: "hypothetical_questions",    description: "Generate hypothetical questions",    parameters: {      type: "object",      properties: {        questions: {          type: "array",          items: {            type: "string",          },        },      },      required: ["questions"],    },  },];const functionCallingModel = new ChatOpenAI({  maxRetries: 0,  model: "gpt-4",}).bind({  functions: functionsSchema,  function_call: { name: "hypothetical_questions" },});const chain = RunnableSequence.from([  { content: (doc: Document) => doc.pageContent },  PromptTemplate.fromTemplate(    `Generate a list of 3 hypothetical questions that the below document could be used to answer:\n\n{content}`  ),  functionCallingModel,  new JsonKeyOutputFunctionsParser<string[]>({ attrName: "questions" }),]);const hypotheticalQuestions = await chain.batch(docs, {  maxConcurrency: 5,});const idKey = "doc_id";const docIds = docs.map((_) => uuid.v4());const hypotheticalQuestionDocs = hypotheticalQuestions  .map((questionArray, i) => {    const questionDocuments = questionArray.map((question) => {      const questionDocument = new Document({        pageContent: question,        metadata: {          [idKey]: docIds[i],        },      });      return questionDocument;    });    return questionDocuments;  })  .flat();// The byteStore to use to store the original chunksconst byteStore = new InMemoryStore<Uint8Array>();// The vectorstore to use to index the child chunksconst vectorstore = await FaissStore.fromDocuments(  hypotheticalQuestionDocs,  new OpenAIEmbeddings());const retriever = new MultiVectorRetriever({  vectorstore,  byteStore,  idKey,});const keyValuePairs: [string, Document][] = docs.map((originalDoc, i) => [  docIds[i],  originalDoc,]);// Use the retriever to add the original chunks to the document storeawait retriever.docstore.mset(keyValuePairs);// We could also add the original chunks to the vectorstore if we wish// const taggedOriginalDocs = docs.map((doc, i) => {//   doc.metadata[idKey] = docIds[i];//   return doc;// });// retriever.vectorstore.addDocuments(taggedOriginalDocs);// Vectorstore alone retrieves the small chunksconst vectorstoreResult = await retriever.vectorstore.similaritySearch(  "justice breyer");console.log(vectorstoreResult[0].pageContent);/*  "What measures will be taken to crack down on corporations overcharging American businesses and consumers?"*/// Retriever returns larger resultconst retrieverResult = await retriever.invoke("justice breyer");console.log(retrieverResult[0].pageContent.length);/*  9770*/

#### API Reference:

*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [MultiVectorRetriever](https://v02.api.js.langchain.com/classes/langchain_retrievers_multi_vector.MultiVectorRetriever.html) from `langchain/retrievers/multi_vector`
*   [FaissStore](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_faiss.FaissStore.html) from `@langchain/community/vectorstores/faiss`
*   [RecursiveCharacterTextSplitter](https://v02.api.js.langchain.com/classes/langchain_textsplitters.RecursiveCharacterTextSplitter.html) from `@langchain/textsplitters`
*   [InMemoryStore](https://v02.api.js.langchain.com/classes/langchain_core_stores.InMemoryStore.html) from `@langchain/core/stores`
*   [TextLoader](https://v02.api.js.langchain.com/classes/langchain_document_loaders_fs_text.TextLoader.html) from `langchain/document_loaders/fs/text`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [RunnableSequence](https://v02.api.js.langchain.com/classes/langchain_core_runnables.RunnableSequence.html) from `@langchain/core/runnables`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`
*   [JsonKeyOutputFunctionsParser](https://v02.api.js.langchain.com/classes/langchain_core_output_parsers_openai_functions.JsonKeyOutputFunctionsParser.html) from `@langchain/core/output_parsers/openai_functions`

Next steps[​](#next-steps "Direct link to Next steps")
------------------------------------------------------

You've now learned a few ways to generate multiple embeddings per document.

Next, check out the individual sections for deeper dives on specific retrievers, the [broader tutorial on RAG](/v0.2/docs/tutorials/rag), or this section to learn how to [create your own custom retriever over any data source](/v0.2/docs/how_to/custom_retriever/).

* * *

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*   [Smaller chunks](#smaller-chunks)
*   [Summary](#summary)
*   [Hypothetical queries](#hypothetical-queries)
*   [Next steps](#next-steps)

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