docs/examples/chains/llm_for_loop.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c475754",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import PromptTemplate\n",
    "from langchain.prompts.base import BaseOutputParser\n",
    "from langchain import OpenAI, LLMChain\n",
    "from langchain.chains.llm_for_loop import LLMForLoopChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "efcdd239",
   "metadata": {},
   "outputs": [],
   "source": [
    "# First we make a chain that generates the list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2b1884f5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from typing import Optional\n",
    "import re\n",
    "class ListOutputParser(BaseOutputParser):\n",
    "    \n",
    "    def __init__(self, regex: Optional[str] = None):\n",
    "        self.regex=regex\n",
    "    \n",
    "    def parse(self, text: str) -> list:\n",
    "        splits = [t for t in text.split(\"\\n\") if t]\n",
    "        if self.regex is not None:\n",
    "            splits = [re.match(self.regex, s).group(1) for s in splits]\n",
    "        return splits"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "b2b7f8fa",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"You are a list maker. Your job is make lists given a certain user input.\n",
    "\n",
    "The format of your lists should be:\n",
    "\n",
    "```\n",
    "List:\n",
    "- Item 1\n",
    "- Item 2\n",
    "...\n",
    "```\n",
    "\n",
    "Begin!:\n",
    "\n",
    "User input: {input}\n",
    "List:\"\"\"\n",
    "output_parser = ListOutputParser(regex=\"- (.*)\")\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"input\"], output_parser=output_parser)\n",
    "\n",
    "chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "2f8ea6ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Tesla', 'Nissan', 'BMW', 'BYD', 'Volkswagen']"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.predict_and_parse(input=\"top 5 ev companies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "1fdfc7cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Next we generate the chain that we run over each item"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "0b8f115a",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"For the following company, explain the origin of their name:\n",
    "\n",
    "Company: {company}\n",
    "Explanation of their name:\"\"\"\n",
    "prompt = PromptTemplate(template=template, input_variables=[\"company\"])\n",
    "\n",
    "explanation_chain = LLMChain(llm=OpenAI(), prompt=prompt, verbose=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6d636881",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: Tesla\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n\\nTesla is a company that specializes in electric cars and renewable energy. The company is named after Nikola Tesla, a Serbian-American inventor and electrical engineer who was born in the 19th century.'"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "explanation_chain.predict(company=\"Tesla\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "3c236dd3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Now we combine them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "941b6389",
   "metadata": {},
   "outputs": [],
   "source": [
    "for_loop_chain = LLMForLoopChain(llm_chain=chain, apply_chain=explanation_chain)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "98c39dbc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: Tesla\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: Nissan\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: BMW\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: BYD\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n",
      "\n",
      "\n",
      "\u001b[1m> Entering new chain...\u001b[0m\n",
      "Prompt after formatting:\n",
      "\u001b[32;1m\u001b[1;3mFor the following company, explain the origin of their name:\n",
      "\n",
      "Company: Volkswagen\n",
      "Explanation of their name:\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "['\\n\\nTesla was named after the Serbian-American inventor Nikola Tesla, who was known for his work in electricity and magnetism.',\n",
       " '\\n\\nNissan is a Japanese company, and their name comes from the Japanese word for \"sun.\"',\n",
       " \"\\n\\nThe company's name is an abbreviation for Bayerische Motoren Werke, which is German for Bavarian Motor Works.\",\n",
       " '\\n\\nThe company\\'s name is derived from the Chinese characters \"Baiyu Dong\", which literally mean \"to catch the rain in the east\". The name is a reference to the company\\'s origins in the city of Shenzhen, in southeastern China.',\n",
       " '\\n\\nVolkswagen is a German car company. The word \"Volkswagen\" means \"people\\'s car\" in German.']"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for_loop_chain.run_list(input=\"top 5 ev companies\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2c1803",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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