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Showing content from https://python.langchain.com/docs/how_to/example_selectors/ below:

How to use example selectors

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(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.

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.

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

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

from langchain_core.example_selectors.base import BaseExampleSelector


class CustomExampleSelector(BaseExampleSelector):
    def __init__(self, examples):
        self.examples = examples

    def add_example(self, example):
        self.examples.append(example)

    def select_examples(self, input_variables):
        
        new_word = input_variables["input"]
        new_word_length = len(new_word)

        
        best_match = None
        smallest_diff = float("inf")

        
        for example in self.examples:
            
            current_diff = abs(len(example["input"]) - new_word_length)

            
            if current_diff < smallest_diff:
                smallest_diff = current_diff
                best_match = example

        return [best_match]

example_selector = CustomExampleSelector(examples)

example_selector.select_examples({"input": "okay"})

[{'input': 'bye', 'output': 'arrivederci'}]

example_selector.add_example({"input": "hand", "output": "mano"})

example_selector.select_examples({"input": "okay"})

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

We can now use this example selector in a prompt

from langchain_core.prompts.few_shot import FewShotPromptTemplate
from langchain_core.prompts.prompt import PromptTemplate

example_prompt = PromptTemplate.from_template("Input: {input} -> Output: {output}")

prompt = 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:

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