{ "cells": [ { "cell_type": "markdown", "id": "a37d9694", "metadata": {}, "source": [ "# Custom Prompt Template\n", "\n", "This notebook goes over how to create a custom prompt template, in case you want to create your own methodology for creating prompts.\n", "\n", "The only two requirements for all prompt templates are:\n", "\n", "1. They have a `input_variables` attribute that exposes what input variables this prompt template expects.\n", "2. They expose a `format` method which takes in keyword arguments corresponding to the expected `input_variables` and returns the formatted prompt.\n", "\n", "Let's imagine that we want to create a prompt template that takes in input variables and formats them into the template AFTER capitalizing them. " ] }, { "cell_type": "code", "execution_count": 3, "id": "26f796e5", "metadata": {}, "outputs": [], "source": [ "from langchain.prompts import BasePromptTemplate\n", "from pydantic import BaseModel" ] }, { "cell_type": "code", "execution_count": 7, "id": "27919e96", "metadata": {}, "outputs": [], "source": [ "class CustomPromptTemplate(BasePromptTemplate, BaseModel):\n", " template: str\n", " \n", " def format(self, **kwargs) -> str:\n", " capitalized_kwargs = {k: v.upper() for k, v in kwargs.items()}\n", " return self.template.format(**capitalized_kwargs)\n", " " ] }, { "cell_type": "markdown", "id": "76d1d84d", "metadata": {}, "source": [ "We can now see that when we use this, the input variables get formatted." ] }, { "cell_type": "code", "execution_count": 8, "id": "eed1ff28", "metadata": {}, "outputs": [], "source": [ "prompt = CustomPromptTemplate(input_variables=[\"foo\"], template=\"Capitalized: {foo}\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "94892a3c", "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Capitalized: LOWERCASE'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "prompt.format(foo=\"lowercase\")" ] }, { "cell_type": "code", "execution_count": null, "id": "d3d9a7c7", "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 }