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Custom example selector

In this tutorial, we'll create a custom example selector that selects examples randomly from a given list of examples.

An ExampleSelector must implement two methods:

  1. An add_example method which takes in an example and adds it into the ExampleSelector
  2. A select_examples method which takes in input variables (which are meant to be user input) and returns a list of examples to use in the few-shot prompt.

Let's implement a custom ExampleSelector that just selects two examples at random.

Note: Take a look at the current set of example selector implementations supported in LangChain here.

Implement custom example selector

from langchain.prompts.example_selector.base import BaseExampleSelector
from typing import Dict, List
import numpy as np

class CustomExampleSelector(BaseExampleSelector):

def __init__(self, examples: List[Dict[str, str]]):
self.examples = examples

def add_example(self, example: Dict[str, str]) -> None:
"""Add new example to store for a key."""

def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:
"""Select which examples to use based on the inputs."""
return np.random.choice(self.examples, size=2, replace=False)

Use custom example selector

examples = [
{"foo": "1"},
{"foo": "2"},
{"foo": "3"}

# Initialize example selector.
example_selector = CustomExampleSelector(examples)

# Select examples
example_selector.select_examples({"foo": "foo"})
# -> array([{'foo': '2'}, {'foo': '3'}], dtype=object)

# Add new example to the set of examples
example_selector.add_example({"foo": "4"})
# -> [{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]

# Select examples
example_selector.select_examples({"foo": "foo"})
# -> array([{'foo': '1'}, {'foo': '4'}], dtype=object)