langchain/docs/examples/prompts/llm_functionality.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "20ac6b98",
   "metadata": {},
   "source": [
    "# LLM Functionality\n",
    "\n",
    "This notebook goes over all the different features of the LLM class in LangChain.\n",
    "\n",
    "We will work with an OpenAI LLM wrapper, although these functionalities should exist for all LLM types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "df924055",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "182b484c",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = OpenAI(model_name=\"text-ada-001\", n=2, best_of=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9695ccfc",
   "metadata": {},
   "source": [
    "**Generate Text:** The most basic functionality an LLM has is just the ability to call it, passing in a string and getting back a string."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "9d12ac26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm(\"Tell me a joke\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7d4d42d",
   "metadata": {},
   "source": [
    "**Generate:** More broadly, you can call it with a list of inputs, getting back a more complete response than just the text. This complete response includes things like multiple top responses, as well as LLM provider specific information"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "f4dc241a",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_result = llm.generate([\"Tell me a joke\", \"Tell me a poem\"]*15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "740392f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(llm_result.generations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ab6cdcf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Generation(text='\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!'),\n",
       " Generation(text='\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side!')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_result.generations[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "4946a778",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Generation(text='\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode\\n\\nHow can I make you my\\nx- Multiplayer gamemode'),\n",
       " Generation(text=\"\\n\\nWhen I was younger\\nI thought that love\\nI was something like a fairytale\\nI would find my prince\\nAnd we would be together\\nForever\\nI was naïve\\nAnd I was wrong\\nLove is not a fairytale\\nIt's something else entirely\\nSomething that should be cherished\\nAnd loved\\nAnd never taken for granted\\nLove is something that you have to work for\\nIt doesn't come easy\\nYou have to sacrifice\\nYour time, your effort\\nAnd sometimes you have to give up \\nYou have to do what's best for yourself\\nAnd sometimes that means giving love up\")]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm_result.generations[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "242e4527",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'token_usage': {'completion_tokens': 3721,\n",
       "  'prompt_tokens': 120,\n",
       "  'total_tokens': 3841}}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Provider specific info\n",
    "llm_result.llm_output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bde8e04f",
   "metadata": {},
   "source": [
    "**Number of Tokens:** You can also estimate how many tokens a piece of text will be in that model. This is useful because models have a context length (and cost more for more tokens), which means you need to be aware of how long the text you are passing in is.\n",
    "\n",
    "Notice that by default the tokens are estimated using a HuggingFace tokenizer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "b623c774",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "llm.get_num_tokens(\"what a joke\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4196efd9",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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