{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "jukit_cell_id": "IagIXdFJ76"
   },
   "source": [
    "# Prompt templates\n",
    "[see](https://langchain.readthedocs.io/en/latest/modules/prompts/getting_started.html)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "jukit_cell_id": "5rNpKSE97a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('\\n'\n",
      " 'I want you to act as a naming consultant for new companies.\\n'\n",
      " '\\n'\n",
      " 'Here are some examples of good company names:\\n'\n",
      " '\\n'\n",
      " '- search engine, Google\\n'\n",
      " '- social media, Facebook\\n'\n",
      " '- video sharing, YouTube\\n'\n",
      " '\\n'\n",
      " 'The name should be short, catchy and easy to remember.\\n'\n",
      " '\\n'\n",
      " 'What is a good name for a company that makes cookie?\\n')\n"
     ]
    }
   ],
   "source": [
    "from langchain import PromptTemplate\n",
    "import pprint as pp\n",
    "\n",
    "template = \"\"\"\n",
    "I want you to act as a naming consultant for new companies.\n",
    "\n",
    "Here are some examples of good company names:\n",
    "\n",
    "- search engine, Google\n",
    "- social media, Facebook\n",
    "- video sharing, YouTube\n",
    "\n",
    "The name should be short, catchy and easy to remember.\n",
    "\n",
    "What is a good name for a company that makes {product}?\n",
    "\"\"\"\n",
    "\n",
    "prompt = PromptTemplate(\n",
    "        input_variables=[\"product\"],\n",
    "        template=template,\n",
    "        )\n",
    "\n",
    "pp.pp(prompt.format(product='cookie'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "jukit_cell_id": "UH7UDNwwOT"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'tell me a funny joke about bats.'"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# without inputs\n",
    "no_input_prompt = PromptTemplate(input_variables=[],\n",
    "                                 template=\"tell me a joke.\")\n",
    "no_input_prompt.format()\n",
    "\n",
    "# with inputs\n",
    "multi_input_prompt = PromptTemplate(\n",
    "        input_variables=[\"adjective\", \"content\"],\n",
    "        template=\"tell me a {adjective} joke about {content}.\" \n",
    "        )\n",
    "multi_input_prompt.format(adjective=\"funny\", content=\"bats\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jukit_cell_id": "m4HfAhbN4T"
   },
   "source": [
    "## Loading prompt templates from LangChainHub "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "jukit_cell_id": "6sAHvM0Vrt"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know.\n",
      "\n",
      "Current conversation:\n",
      "\n",
      "Human: what is 1 + 1?\n",
      "AI:\n"
     ]
    }
   ],
   "source": [
    "from langchain.prompts import load_prompt\n",
    "\n",
    "prompt=load_prompt(\"lc://prompts/conversation/prompt.json\")\n",
    "#NOTE: is there a helper to quickly build a history ? \n",
    "print(prompt.format(history=\"\", input=\"what is 1 + 1?\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jukit_cell_id": "u2xOTHeA5E"
   },
   "source": [
    "## Pass few shot examples to prompt template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "jukit_cell_id": "KO5IXCuzjw"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give the antonym of every input\n",
      "\n",
      "\n",
      "Word: happy\n",
      "Antonym: sad\n",
      "\n",
      "\n",
      "\n",
      "Word: tall\n",
      "Antonym: short\n",
      "\n",
      "\n",
      "Word: fast\n",
      "Antonym:\n"
     ]
    }
   ],
   "source": [
    "from langchain import FewShotPromptTemplate\n",
    "\n",
    "# create a list of few shot examples\n",
    "examples = [\n",
    "        {\"word\": \"happy\", \"antonym\": \"sad\"},\n",
    "        {\"word\": \"tall\", \"antonym\": \"short\"},\n",
    "        ]\n",
    "\n",
    "# next e specify a template for format the examples\n",
    "# we use PromptTemplate class\n",
    "example_formatter_template = \"\"\"\n",
    "Word: {word}\n",
    "Antonym: {antonym}\n",
    "\"\"\"\n",
    "example_pr = PromptTemplate(\n",
    "        input_variables=[\"word\", \"antonym\"],\n",
    "        template=example_formatter_template,\n",
    "        )\n",
    "\n",
    "# now we can use FewShotPromptTemplate\n",
    "few_shot_prompt = FewShotPromptTemplate(\n",
    "        # examples we want to insert in prompt\n",
    "        examples=examples,\n",
    "        # how we want examples to be formatted in prompt\n",
    "        example_prompt=example_pr,\n",
    "        # The prefix is some text that goes before the examples in the prompt.\n",
    "        # Usually, this consists of intructions.\n",
    "        prefix=\"Give the antonym of every input\",\n",
    "        #The suffix is some text that goes after the examples in the prompt.\n",
    "        suffix=\"Word: {input}\\nAntonym:\",\n",
    "        # The input variables are the variables that the overall prompt expects.\n",
    "        input_variables=[\"input\"],\n",
    "        # The example_separator is the string we will use to join the prefix, examples, and suffix together with.\n",
    "        example_separator=\"\\n\\n\",\n",
    "        )\n",
    "# generate few shot prompt using input\n",
    "print(few_shot_prompt.format(input=\"fast\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "jukit_cell_id": "fV1qxeDncc"
   },
   "source": [
    "## Select examples from prompt template\n",
    "\n",
    "- for a large number of exaamples use ExampleSelector to select a subset of\n",
    "  most informative ones for language model.\n",
    "- LengthBasedExampleSelector selects examples based on length of input.\n",
    "  practical to to construct prompt that do not extend over context window\n",
    "  based on input length"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "jukit_cell_id": "uuIdYaJ4wD"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Give the antonym of every input\n",
      "\n",
      "\n",
      "Word: happy\n",
      "Antonym: sad\n",
      "\n",
      "\n",
      "\n",
      "Word: tall\n",
      "Antonym: short\n",
      "\n",
      "\n",
      "\n",
      "Word: energetic\n",
      "Antonym: lethargic\n",
      "\n",
      "\n",
      "\n",
      "Word: sunny\n",
      "Antonym: gloomy\n",
      "\n",
      "\n",
      "Word: big\n",
      "Antonym:\n",
      "----------\n",
      "Give the antonym of every input\n",
      "\n",
      "\n",
      "Word: happy\n",
      "Antonym: sad\n",
      "\n",
      "\n",
      "Word: big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\n",
      "Antonym:\n"
     ]
    }
   ],
   "source": [
    "from langchain.prompts.example_selector import LengthBasedExampleSelector\n",
    "\n",
    "#These are a lot of examples of a pretend task of creating antonyms.\n",
    "examples = [\n",
    "    {\"word\": \"happy\", \"antonym\": \"sad\"},\n",
    "    {\"word\": \"tall\", \"antonym\": \"short\"},\n",
    "    {\"word\": \"energetic\", \"antonym\": \"lethargic\"},\n",
    "    {\"word\": \"sunny\", \"antonym\": \"gloomy\"},\n",
    "    {\"word\": \"windy\", \"antonym\": \"calm\"},\n",
    "]\n",
    "\n",
    "example_selector = LengthBasedExampleSelector(\n",
    "        examples=examples,\n",
    "        # This is the PromptTemplate being used to format the examples.\n",
    "        example_prompt=example_pr,\n",
    "        # This is the maximum length that the formatted examples should be.\n",
    "        # Length is measured by the get_text_length function below.\n",
    "        max_length=30,\n",
    "        )\n",
    "\n",
    "# We can now use the `example_selector` to create a `FewShotPromptTemplate`.\n",
    "dynamic_prompt = FewShotPromptTemplate(\n",
    "    # We provide an ExampleSelector instead of examples.\n",
    "    example_selector=example_selector,\n",
    "    example_prompt=example_pr,\n",
    "    prefix=\"Give the antonym of every input\",\n",
    "    suffix=\"Word: {input}\\nAntonym:\",\n",
    "    input_variables=[\"input\"],\n",
    "    example_separator=\"\\n\\n\",\n",
    ")\n",
    "\n",
    "# We can now generate a prompt using the `format` method.\n",
    "print(dynamic_prompt.format(input=\"big\"))\n",
    "\n",
    "print(\"----------\")\n",
    "\n",
    "# In contrast, if we provide a very long input, the LengthBasedExampleSelector\n",
    "# will select fewer examples to include in the prompt.\n",
    "long_string = \"big and huge and massive and large and gigantic and tall and much much much much much bigger than everything else\"\n",
    "print(dynamic_prompt.format(input=long_string))"
   ]
  }
 ],
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