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*   [Introduction](/v0.2/docs/introduction)
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    *   [Build a Question Answering application over a Graph Database](/v0.2/docs/tutorials/graph)
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*   [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)
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    *   [How to use few shot examples in chat models](/v0.2/docs/how_to/few_shot_examples_chat)
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    *   [How to pass through arguments from one step to the next](/v0.2/docs/how_to/passthrough)
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    *   [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)
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    *   [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)
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    *   [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)
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    *   [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/)
        
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*   Versions
    
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    *   [Packages](/v0.2/docs/versions/packages)
*   [Security](/v0.2/docs/security)

*   [](/v0.2/)
*   [How-to guides](/v0.2/docs/how_to/)
*   How to select examples by similarity

On this page

How to select examples by similarity
====================================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Prompt templates](/v0.2/docs/concepts/#prompt-templates)
*   [Example selectors](/v0.2/docs/how_to/example_selectors)
*   [Vector stores](/v0.2/docs/concepts#vectorstores)

This object selects examples based on similarity to the inputs. It does this by finding the examples with the embeddings that have the greatest cosine similarity with the inputs.

The fields of the examples object will be used as parameters to format the `examplePrompt` passed to the `FewShotPromptTemplate`. Each example should therefore contain all required fields for the example prompt you are using.

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 { OpenAIEmbeddings } from "@langchain/openai";import { HNSWLib } from "@langchain/community/vectorstores/hnswlib";import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";import { SemanticSimilarityExampleSelector } from "@langchain/core/example_selectors";// Create a prompt template that will be used to format the examples.const examplePrompt = PromptTemplate.fromTemplate(  "Input: {input}\nOutput: {output}");// Create a SemanticSimilarityExampleSelector that will be used to select the examples.const exampleSelector = await SemanticSimilarityExampleSelector.fromExamples(  [    { input: "happy", output: "sad" },    { input: "tall", output: "short" },    { input: "energetic", output: "lethargic" },    { input: "sunny", output: "gloomy" },    { input: "windy", output: "calm" },  ],  new OpenAIEmbeddings(),  HNSWLib,  { k: 1 });// Create a FewShotPromptTemplate that will use the example selector.const dynamicPrompt = new FewShotPromptTemplate({  // We provide an ExampleSelector instead of examples.  exampleSelector,  examplePrompt,  prefix: "Give the antonym of every input",  suffix: "Input: {adjective}\nOutput:",  inputVariables: ["adjective"],});// Input is about the weather, so should select eg. the sunny/gloomy exampleconsole.log(await dynamicPrompt.format({ adjective: "rainy" }));/*  Give the antonym of every input  Input: sunny  Output: gloomy  Input: rainy  Output:*/// Input is a measurement, so should select the tall/short exampleconsole.log(await dynamicPrompt.format({ adjective: "large" }));/*  Give the antonym of every input  Input: tall  Output: short  Input: large  Output:*/

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [HNSWLib](https://v02.api.js.langchain.com/classes/langchain_community_vectorstores_hnswlib.HNSWLib.html) from `@langchain/community/vectorstores/hnswlib`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [FewShotPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.FewShotPromptTemplate.html) from `@langchain/core/prompts`
*   [SemanticSimilarityExampleSelector](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.SemanticSimilarityExampleSelector.html) from `@langchain/core/example_selectors`

By default, each field in the examples object is concatenated together, embedded, and stored in the vectorstore for later similarity search against user queries.

If you only want to embed specific keys (e.g., you only want to search for examples that have a similar query to the one the user provides), you can pass an `inputKeys` array in the final `options` parameter.

Loading from an existing vectorstore[​](#loading-from-an-existing-vectorstore "Direct link to Loading from an existing vectorstore")
------------------------------------------------------------------------------------------------------------------------------------

You can also use a pre-initialized vector store by passing an instance to the `SemanticSimilarityExampleSelector` constructor directly, as shown below. You can also add more examples via the `addExample` method:

    // Ephemeral, in-memory vector store for demo purposesimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";import { SemanticSimilarityExampleSelector } from "@langchain/core/example_selectors";const embeddings = new OpenAIEmbeddings();const memoryVectorStore = new MemoryVectorStore(embeddings);const examples = [  {    query: "healthy food",    output: `galbi`,  },  {    query: "healthy food",    output: `schnitzel`,  },  {    query: "foo",    output: `bar`,  },];const exampleSelector = new SemanticSimilarityExampleSelector({  vectorStore: memoryVectorStore,  k: 2,  // Only embed the "query" key of each example  inputKeys: ["query"],});for (const example of examples) {  // Format and add an example to the underlying vector store  await exampleSelector.addExample(example);}// Create a prompt template that will be used to format the examples.const examplePrompt = PromptTemplate.fromTemplate(`<example>  <user_input>    {query}  </user_input>  <output>    {output}  </output></example>`);// Create a FewShotPromptTemplate that will use the example selector.const dynamicPrompt = new FewShotPromptTemplate({  // We provide an ExampleSelector instead of examples.  exampleSelector,  examplePrompt,  prefix: `Answer the user's question, using the below examples as reference:`,  suffix: "User question: {query}",  inputVariables: ["query"],});const formattedValue = await dynamicPrompt.format({  query: "What is a healthy food?",});console.log(formattedValue);/*Answer the user's question, using the below examples as reference:<example>  <user_input>    healthy  </user_input>  <output>    galbi  </output></example><example>  <user_input>    healthy  </user_input>  <output>    schnitzel  </output></example>User question: What is a healthy food?*/const model = new ChatOpenAI({});const chain = dynamicPrompt.pipe(model);const result = await chain.invoke({ query: "What is a healthy food?" });console.log(result);/*  AIMessage {    content: 'A healthy food can be galbi or schnitzel.',    additional_kwargs: { function_call: undefined }  }*/

#### API Reference:

*   [MemoryVectorStore](https://v02.api.js.langchain.com/classes/langchain_vectorstores_memory.MemoryVectorStore.html) from `langchain/vectorstores/memory`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [FewShotPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.FewShotPromptTemplate.html) from `@langchain/core/prompts`
*   [SemanticSimilarityExampleSelector](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.SemanticSimilarityExampleSelector.html) from `@langchain/core/example_selectors`

Metadata filtering[​](#metadata-filtering "Direct link to Metadata filtering")
------------------------------------------------------------------------------

When adding examples, each field is available as metadata in the produced document. If you would like further control over your search space, you can add extra fields to your examples and pass a `filter` parameter when initializing your selector:

    // Ephemeral, in-memory vector store for demo purposesimport { MemoryVectorStore } from "langchain/vectorstores/memory";import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";import { Document } from "@langchain/core/documents";import { SemanticSimilarityExampleSelector } from "@langchain/core/example_selectors";const embeddings = new OpenAIEmbeddings();const memoryVectorStore = new MemoryVectorStore(embeddings);const examples = [  {    query: "healthy food",    output: `lettuce`,    food_type: "vegetable",  },  {    query: "healthy food",    output: `schnitzel`,    food_type: "veal",  },  {    query: "foo",    output: `bar`,    food_type: "baz",  },];const exampleSelector = new SemanticSimilarityExampleSelector({  vectorStore: memoryVectorStore,  k: 2,  // Only embed the "query" key of each example  inputKeys: ["query"],  // Filter type will depend on your specific vector store.  // See the section of the docs for the specific vector store you are using.  filter: (doc: Document) => doc.metadata.food_type === "vegetable",});for (const example of examples) {  // Format and add an example to the underlying vector store  await exampleSelector.addExample(example);}// Create a prompt template that will be used to format the examples.const examplePrompt = PromptTemplate.fromTemplate(`<example>  <user_input>    {query}  </user_input>  <output>    {output}  </output></example>`);// Create a FewShotPromptTemplate that will use the example selector.const dynamicPrompt = new FewShotPromptTemplate({  // We provide an ExampleSelector instead of examples.  exampleSelector,  examplePrompt,  prefix: `Answer the user's question, using the below examples as reference:`,  suffix: "User question:\n{query}",  inputVariables: ["query"],});const model = new ChatOpenAI({});const chain = dynamicPrompt.pipe(model);const result = await chain.invoke({  query: "What is exactly one type of healthy food?",});console.log(result);/*  AIMessage {    content: 'One type of healthy food is lettuce.',    additional_kwargs: { function_call: undefined }  }*/

#### API Reference:

*   [MemoryVectorStore](https://v02.api.js.langchain.com/classes/langchain_vectorstores_memory.MemoryVectorStore.html) from `langchain/vectorstores/memory`
*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [FewShotPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.FewShotPromptTemplate.html) from `@langchain/core/prompts`
*   [Document](https://v02.api.js.langchain.com/classes/langchain_core_documents.Document.html) from `@langchain/core/documents`
*   [SemanticSimilarityExampleSelector](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.SemanticSimilarityExampleSelector.html) from `@langchain/core/example_selectors`

Custom vectorstore retrievers[​](#custom-vectorstore-retrievers "Direct link to Custom vectorstore retrievers")
---------------------------------------------------------------------------------------------------------------

You can also pass a vectorstore retriever instead of a vectorstore. One way this could be useful is if you want to use retrieval besides similarity search such as maximal marginal relevance:

    /* eslint-disable @typescript-eslint/no-non-null-assertion */// Requires a vectorstore that supports maximal marginal relevance searchimport { Pinecone } from "@pinecone-database/pinecone";import { OpenAIEmbeddings, ChatOpenAI } from "@langchain/openai";import { PineconeStore } from "@langchain/pinecone";import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";import { SemanticSimilarityExampleSelector } from "@langchain/core/example_selectors";const pinecone = new Pinecone();const pineconeIndex = pinecone.Index(process.env.PINECONE_INDEX!);const pineconeVectorstore = await PineconeStore.fromExistingIndex(  new OpenAIEmbeddings(),  { pineconeIndex });const pineconeMmrRetriever = pineconeVectorstore.asRetriever({  searchType: "mmr",  k: 2,});const examples = [  {    query: "healthy food",    output: `lettuce`,    food_type: "vegetable",  },  {    query: "healthy food",    output: `schnitzel`,    food_type: "veal",  },  {    query: "foo",    output: `bar`,    food_type: "baz",  },];const exampleSelector = new SemanticSimilarityExampleSelector({  vectorStoreRetriever: pineconeMmrRetriever,  // Only embed the "query" key of each example  inputKeys: ["query"],});for (const example of examples) {  // Format and add an example to the underlying vector store  await exampleSelector.addExample(example);}// Create a prompt template that will be used to format the examples.const examplePrompt = PromptTemplate.fromTemplate(`<example>  <user_input>    {query}  </user_input>  <output>    {output}  </output></example>`);// Create a FewShotPromptTemplate that will use the example selector.const dynamicPrompt = new FewShotPromptTemplate({  // We provide an ExampleSelector instead of examples.  exampleSelector,  examplePrompt,  prefix: `Answer the user's question, using the below examples as reference:`,  suffix: "User question:\n{query}",  inputVariables: ["query"],});const model = new ChatOpenAI({});const chain = dynamicPrompt.pipe(model);const result = await chain.invoke({  query: "What is exactly one type of healthy food?",});console.log(result);/*  AIMessage {    content: 'lettuce.',    additional_kwargs: { function_call: undefined }  }*/

#### API Reference:

*   [OpenAIEmbeddings](https://v02.api.js.langchain.com/classes/langchain_openai.OpenAIEmbeddings.html) from `@langchain/openai`
*   [ChatOpenAI](https://v02.api.js.langchain.com/classes/langchain_openai.ChatOpenAI.html) from `@langchain/openai`
*   [PineconeStore](https://v02.api.js.langchain.com/classes/langchain_pinecone.PineconeStore.html) from `@langchain/pinecone`
*   [PromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.PromptTemplate.html) from `@langchain/core/prompts`
*   [FewShotPromptTemplate](https://v02.api.js.langchain.com/classes/langchain_core_prompts.FewShotPromptTemplate.html) from `@langchain/core/prompts`
*   [SemanticSimilarityExampleSelector](https://v02.api.js.langchain.com/classes/langchain_core_example_selectors.SemanticSimilarityExampleSelector.html) from `@langchain/core/example_selectors`

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

You've now learned a bit about using similarity in an example selector.

Next, check out this guide on how to use a [length-based example selector](/v0.2/docs/how_to/example_selectors_length_based).

* * *

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How to select examples by length

](/v0.2/docs/how_to/example_selectors_length_based)[

Next

How to use reference examples

](/v0.2/docs/how_to/extraction_examples)

*   [Loading from an existing vectorstore](#loading-from-an-existing-vectorstore)
*   [Metadata filtering](#metadata-filtering)
*   [Custom vectorstore retrievers](#custom-vectorstore-retrievers)
*   [Next steps](#next-steps)

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