{ "cells": [ { "cell_type": "markdown", "id": "f897c784", "metadata": {}, "source": [ "# Custom ExampleSelector\n", "\n", "This notebook goes over how to implement a custom ExampleSelector. ExampleSelectors are used to select examples to use in few shot prompts.\n", "\n", "An ExampleSelector must implement two methods:\n", "\n", "1. An `add_example` method which takes in an example and adds it into the ExampleSelector\n", "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.\n", "\n", "\n", "Let's implement a custom ExampleSelector that just selects two examples at random." ] }, { "cell_type": "code", "execution_count": 1, "id": "1a945da1", "metadata": {}, "outputs": [], "source": [ "from langchain.prompts.example_selector.base import BaseExampleSelector\n", "from typing import Dict, List\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": 2, "id": "62cf0ad7", "metadata": {}, "outputs": [], "source": [ "class CustomExampleSelector(BaseExampleSelector):\n", " \n", " def __init__(self, examples: List[Dict[str, str]]):\n", " self.examples = examples\n", " \n", " def add_example(self, example: Dict[str, str]) -> None:\n", " \"\"\"Add new example to store for a key.\"\"\"\n", " self.examples.append(example)\n", "\n", " def select_examples(self, input_variables: Dict[str, str]) -> List[dict]:\n", " \"\"\"Select which examples to use based on the inputs.\"\"\"\n", " return np.random.choice(self.examples, size=2, replace=False)" ] }, { "cell_type": "code", "execution_count": 3, "id": "242d3213", "metadata": {}, "outputs": [], "source": [ "examples = [{\"foo\": \"1\"}, {\"foo\": \"2\"}, {\"foo\": \"3\"}]\n", "example_selector = CustomExampleSelector(examples)" ] }, { "cell_type": "markdown", "id": "2a038065", "metadata": {}, "source": [ "Let's now try it out! We can select some examples and try adding examples." ] }, { "cell_type": "code", "execution_count": 4, "id": "74fbbef5", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([{'foo': '2'}, {'foo': '3'}], dtype=object)" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_selector.select_examples({\"foo\": \"foo\"})" ] }, { "cell_type": "code", "execution_count": 5, "id": "9bbb5421", "metadata": {}, "outputs": [], "source": [ "example_selector.add_example({\"foo\": \"4\"})" ] }, { "cell_type": "code", "execution_count": 6, "id": "c0eb9f22", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[{'foo': '1'}, {'foo': '2'}, {'foo': '3'}, {'foo': '4'}]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_selector.examples" ] }, { "cell_type": "code", "execution_count": 7, "id": "cc39b1e3", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([{'foo': '1'}, {'foo': '4'}], dtype=object)" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "example_selector.select_examples({\"foo\": \"foo\"})" ] }, { "cell_type": "code", "execution_count": null, "id": "1739dd96", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 5 }