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*   How to select examples by similarity

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

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.

    from langchain_chroma import Chromafrom langchain_core.example_selectors import SemanticSimilarityExampleSelectorfrom langchain_core.prompts import FewShotPromptTemplate, PromptTemplatefrom langchain_openai import OpenAIEmbeddingsexample_prompt = PromptTemplate(    input_variables=["input", "output"],    template="Input: {input}\nOutput: {output}",)# Examples of a pretend task of creating antonyms.examples = [    {"input": "happy", "output": "sad"},    {"input": "tall", "output": "short"},    {"input": "energetic", "output": "lethargic"},    {"input": "sunny", "output": "gloomy"},    {"input": "windy", "output": "calm"},]

**API Reference:**[SemanticSimilarityExampleSelector](https://api.python.langchain.com/en/latest/example_selectors/langchain_core.example_selectors.semantic_similarity.SemanticSimilarityExampleSelector.html) | [FewShotPromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.few_shot.FewShotPromptTemplate.html) | [PromptTemplate](https://api.python.langchain.com/en/latest/prompts/langchain_core.prompts.prompt.PromptTemplate.html) | [OpenAIEmbeddings](https://api.python.langchain.com/en/latest/embeddings/langchain_openai.embeddings.base.OpenAIEmbeddings.html)

    example_selector = SemanticSimilarityExampleSelector.from_examples(    # The list of examples available to select from.    examples,    # The embedding class used to produce embeddings which are used to measure semantic similarity.    OpenAIEmbeddings(),    # The VectorStore class that is used to store the embeddings and do a similarity search over.    Chroma,    # The number of examples to produce.    k=1,)similar_prompt = FewShotPromptTemplate(    # We provide an ExampleSelector instead of examples.    example_selector=example_selector,    example_prompt=example_prompt,    prefix="Give the antonym of every input",    suffix="Input: {adjective}\nOutput:",    input_variables=["adjective"],)

    # Input is a feeling, so should select the happy/sad exampleprint(similar_prompt.format(adjective="worried"))

    Give the antonym of every inputInput: happyOutput: sadInput: worriedOutput:

    # Input is a measurement, so should select the tall/short exampleprint(similar_prompt.format(adjective="large"))

    Give the antonym of every inputInput: tallOutput: shortInput: largeOutput:

    # You can add new examples to the SemanticSimilarityExampleSelector as wellsimilar_prompt.example_selector.add_example(    {"input": "enthusiastic", "output": "apathetic"})print(similar_prompt.format(adjective="passionate"))

    Give the antonym of every inputInput: enthusiasticOutput: apatheticInput: passionateOutput:

[Edit this page](https://github.com/langchain-ai/langchain/edit/master/docs/docs/how_to/example_selectors_similarity.ipynb)

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