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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)
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    *   [Build an Extraction Chain](/v0.2/docs/tutorials/extraction)
    *   [Summarize Text](/v0.2/docs/tutorials/summarization)
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    *   [Build a Local RAG Application](/v0.2/docs/tutorials/local_rag)
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    *   [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)
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    *   [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)
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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 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)
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    *   [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 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)
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*   [How-to guides](/v0.2/docs/how_to/)
*   How to use example selectors

On this page

How to use example selectors
============================

Prerequisites

This guide assumes familiarity with the following concepts:

*   [Prompt templates](/v0.2/docs/concepts/#prompt-templates)
*   [Few-shot examples](/v0.2/docs/how_to/few_shot_examples)

If you have a large number of examples, you may need to select which ones to include in the prompt. The Example Selector is the class responsible for doing so.

The base interface is defined as below:

    class BaseExampleSelector {  addExample(example: Example): Promise<void | string>;  selectExamples(input_variables: Example): Promise<Example[]>;}

The only method it needs to define is a `selectExamples` method. This takes in the input variables and then returns a list of examples. It is up to each specific implementation as to how those examples are selected.

LangChain has a few different types of example selectors. For an overview of all these types, see the below table.

In this guide, we will walk through creating a custom example selector.

Examples[​](#examples "Direct link to Examples")
------------------------------------------------

In order to use an example selector, we need to create a list of examples. These should generally be example inputs and outputs. For this demo purpose, let’s imagine we are selecting examples of how to translate English to Italian.

    const examples = [  { input: "hi", output: "ciao" },  { input: "bye", output: "arrivaderci" },  { input: "soccer", output: "calcio" },];

Custom Example Selector[​](#custom-example-selector "Direct link to Custom Example Selector")
---------------------------------------------------------------------------------------------

Let’s write an example selector that chooses what example to pick based on the length of the word.

    import { BaseExampleSelector } from "@langchain/core/example_selectors";import { Example } from "@langchain/core/prompts";class CustomExampleSelector extends BaseExampleSelector {  private examples: Example[];  constructor(examples: Example[]) {    super();    this.examples = examples;  }  async addExample(example: Example): Promise<void | string> {    this.examples.push(example);    return;  }  async selectExamples(inputVariables: Example): Promise<Example[]> {    // This assumes knowledge that part of the input will be a 'text' key    const newWord = inputVariables.input;    const newWordLength = newWord.length;    // Initialize variables to store the best match and its length difference    let bestMatch: Example | null = null;    let smallestDiff = Infinity;    // Iterate through each example    for (const example of this.examples) {      // Calculate the length difference with the first word of the example      const currentDiff = Math.abs(example.input.length - newWordLength);      // Update the best match if the current one is closer in length      if (currentDiff < smallestDiff) {        smallestDiff = currentDiff;        bestMatch = example;      }    }    return bestMatch ? [bestMatch] : [];  }}

    const exampleSelector = new CustomExampleSelector(examples);

    await exampleSelector.selectExamples({ input: "okay" });

    [ { input: "bye", output: "arrivaderci" } ]

    await exampleSelector.addExample({ input: "hand", output: "mano" });

    await exampleSelector.selectExamples({ input: "okay" });

    [ { input: "hand", output: "mano" } ]

Use in a Prompt[​](#use-in-a-prompt "Direct link to Use in a Prompt")
---------------------------------------------------------------------

We can now use this example selector in a prompt

    import { PromptTemplate, FewShotPromptTemplate } from "@langchain/core/prompts";const examplePrompt = PromptTemplate.fromTemplate(  "Input: {input} -> Output: {output}");

    const prompt = new FewShotPromptTemplate({  exampleSelector,  examplePrompt,  suffix: "Input: {input} -> Output:",  prefix: "Translate the following words from English to Italain:",  inputVariables: ["input"],});console.log(await prompt.format({ input: "word" }));

    Translate the following words from English to Italain:Input: hand -> Output: manoInput: word -> Output:

Example Selector Types[​](#example-selector-types "Direct link to Example Selector Types")
------------------------------------------------------------------------------------------

Name

Description

Similarity

Uses semantic similarity between inputs and examples to decide which examples to choose.

Length

Selects examples based on how many can fit within a certain length

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

You’ve now learned a bit about using example selectors to few shot LLMs.

Next, check out some guides on some other techniques for selecting examples:

*   [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)

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*   [Examples](#examples)
*   [Custom Example Selector](#custom-example-selector)
*   [Use in a Prompt](#use-in-a-prompt)
*   [Example Selector Types](#example-selector-types)
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