langchain/docs/modules/prompts/prompt_templates/examples/prompt_composition.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c79d1cbf",
   "metadata": {},
   "source": [
    "# Prompt Composition\n",
    "\n",
    "This notebook goes over how to compose multiple prompts together. This can be useful when you want to reuse parts of prompts. This can be done with a PipelinePrompt. A PipelinePrompt consists of two main parts:\n",
    "\n",
    "- final_prompt: This is the final prompt that is returned\n",
    "- pipeline_prompts: This is a list of tuples, consisting of a string (`name`) and a Prompt Template. Each PromptTemplate will be formatted and then passed to future prompt templates as a variable with the same name as `name`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4eb8c5e6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts.pipeline import PipelinePromptTemplate\n",
    "from langchain.prompts.prompt import PromptTemplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "67842c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "full_template = \"\"\"{introduction}\n",
    "\n",
    "{example}\n",
    "\n",
    "{start}\"\"\"\n",
    "full_prompt = PromptTemplate.from_template(full_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "11913f4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "introduction_template = \"\"\"You are impersonating {person}.\"\"\"\n",
    "introduction_prompt = PromptTemplate.from_template(introduction_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "bc94cac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "example_template = \"\"\"Here's an example of an interaction: \n",
    "\n",
    "Q: {example_q}\n",
    "A: {example_a}\"\"\"\n",
    "example_prompt = PromptTemplate.from_template(example_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "e89c4dd7",
   "metadata": {},
   "outputs": [],
   "source": [
    "start_template = \"\"\"Now, do this for real!\n",
    "\n",
    "Q: {input}\n",
    "A:\"\"\"\n",
    "start_prompt = PromptTemplate.from_template(start_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fa029e4b",
   "metadata": {},
   "outputs": [],
   "source": [
    "input_prompts = [\n",
    "    (\"introduction\", introduction_prompt),\n",
    "    (\"example\", example_prompt),\n",
    "    (\"start\", start_prompt)\n",
    "]\n",
    "pipeline_prompt = PipelinePromptTemplate(final_prompt=full_prompt, pipeline_prompts=input_prompts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "674ea983",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['example_a', 'person', 'example_q', 'input']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline_prompt.input_variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f1fa0925",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "You are impersonating Elon Musk.\n",
      "Here's an example of an interaction: \n",
      "\n",
      "Q: What's your favorite car?\n",
      "A: Telsa\n",
      "Now, do this for real!\n",
      "\n",
      "Q: What's your favorite social media site?\n",
      "A:\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(pipeline_prompt.format(\n",
    "    person=\"Elon Musk\",\n",
    "    example_q=\"What's your favorite car?\",\n",
    "    example_a=\"Telsa\",\n",
    "    input=\"What's your favorite social media site?\"\n",
    "))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "047c2b0a",
   "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": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}