How to use example selectors
Source: langchain

---sidebar_position: 1---# How to use example selectorsIf 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:```pythonclass BaseExampleSelector(ABC):    """Interface for selecting examples to include in prompts."""    @abstractmethod    def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:        """Select which examples to use based on the inputs."""            @abstractmethod    def add_example(self, example: Dict[str, str]) -> Any:        """Add new example to store."""```The only method it needs to define is a ``select_examples`` 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.## ExamplesIn 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.```pythonexamples = [    {"input": "hi", "output": "ciao"},    {"input": "bye", "output": "arrivederci"},    {"input": "soccer", "output": "calcio"},]```## Custom Example SelectorLet's write an example selector that chooses what example to pick based on the length of the word.```pythonfrom langchain_core.example_selectors.base import BaseExampleSelectorclass CustomExampleSelector(BaseExampleSelector):    def __init__(self, examples):        self.examples = examples    def add_example(self, example):        self.examples.append(example)    def select_examples(self, input_variables):        # This assumes knowledge that part of the input will be a 'text' key        new_word = input_variables["input"]        new_word_length = len(new_word)        # Initialize variables to store the best match and its length difference        best_match = None        smallest_diff = float("inf")        # Iterate through each example        for example in self.examples:            # Calculate the length difference with the first word of the example            current_diff = abs(len(example["input"]) - new_word_length)            # Update the best match if the current one is closer in length            if current_diff < smallest_diff:                smallest_diff = current_diff                best_match = example        return [best_match]``````pythonexample_selector = CustomExampleSelector(examples)``````pythonexample_selector.select_examples({"input": "okay"})```    [{'input': 'bye', 'output': 'arrivederci'}]```pythonexample_selector.add_example({"input": "hand", "output": "mano"})``````pythonexample_selector.select_examples({"input": "okay"})```    [{'input': 'hand', 'output': 'mano'}]## Use in a PromptWe can now use this example selector in a prompt```pythonfrom langchain_core.prompts.few_shot import FewShotPromptTemplatefrom langchain_core.prompts.prompt import PromptTemplateexample_prompt = PromptTemplate.from_template("Input: {input} -> Output: {output}")``````pythonprompt = FewShotPromptTemplate(    example_selector=example_selector,    example_prompt=example_prompt,    suffix="Input: {input} -> Output:",    prefix="Translate the following words from English to Italian:",    input_variables=["input"],)print(prompt.format(input="word"))```    Translate the following words from English to Italian:        Input: hand -> Output: mano        Input: word -> Output:## Example Selector Types| Name       | Description                                                                                 ||------------|---------------------------------------------------------------------------------------------|| Similarity | Uses semantic similarity between inputs and examples to decide which examples to choose.    || MMR        | Uses Max Marginal Relevance between inputs and examples to decide which examples to choose. || Length     | Selects examples based on how many can fit within a certain length                          || Ngram      | Uses ngram overlap between inputs and examples to decide which examples to choose.          |```python```