course_1/models_prompts_and_output_parsesrs.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# LangChain: Models, Prompts and Output Parsers\n",
    "\n",
    "\n",
    "## Outline\n",
    "\n",
    " * Direct API calls to OpenAI\n",
    " * API calls through LangChain:\n",
    "   * Prompts\n",
    "   * Models\n",
    "   * Output parsers"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get your [OpenAI API Key](https://platform.openai.com/account/api-keys)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install python-dotenv\n",
    "#!pip install openai"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "from openai import OpenAI\n",
    "import os\n",
    "\n",
    "client = OpenAI(\n",
    "    # defaults to os.environ.get(\"OPENAI_API_KEY\")\n",
    "    api_key=os.getenv(\"OPENAI_API_KEY\")\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: LLM's do not always produce the same results. When executing the code in your notebook, you may get slightly different answers that those in the video."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat API : OpenAI\n",
    "\n",
    "Let's start with a direct API calls to OpenAI."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_completion(prompt, model=\"gpt-3.5-turbo\"):\n",
    "    messages = [{\"role\": \"user\", \"content\": prompt}]\n",
    "    response = client.chat.completions.create(\n",
    "        model=model,\n",
    "        messages=messages,\n",
    "        temperature=0,\n",
    "    )\n",
    "    return response.choices[0].message.content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'The capital of France is Paris.'"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "get_completion(\"What is the capital of France?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_email = \"\"\"\n",
    "Arrr, I be fuming that me blender lid \\\n",
    "flew off and splattered me kitchen walls \\\n",
    "with smoothie! And to make matters worse,\\\n",
    "the warranty don't cover the cost of \\\n",
    "cleaning up me kitchen. I need yer help \\\n",
    "right now, matey!\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "style = \"\"\"American english in calm and respectful tone\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Translate the text     that is delimited by triple backticks     into a style that is American english in calm and respectful tone. \n",
      "    text: ```\n",
      "Arrr, I be fuming that me blender lid flew off and splattered me kitchen walls with smoothie! And to make matters worse,the warranty don't cover the cost of cleaning up me kitchen. I need yer help right now, matey!\n",
      "```\n",
      "    \n"
     ]
    }
   ],
   "source": [
    "prompt = f\"\"\"Translate the text \\\n",
    "    that is delimited by triple backticks \\\n",
    "    into a style that is {style}. \n",
    "    text: ```{customer_email}```\n",
    "    \"\"\"\n",
    "print(prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"I'm really frustrated that my blender lid flew off and splattered my kitchen walls with smoothie! And to make matters worse, the warranty doesn't cover the cost of cleaning up my kitchen. I could really use your help right now, friend.\""
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = get_completion(prompt)\n",
    "response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chat API : LangChain\n",
    "\n",
    "Let's try how we can do the same using LangChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install --upgrade langchain"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ChatOpenAI(client=<openai.resources.chat.completions.Completions object at 0x7f3cf59e7f50>, async_client=<openai.resources.chat.completions.AsyncCompletions object at 0x7f3cf5a07e10>, temperature=0.0, openai_api_key=SecretStr('**********'), openai_proxy='')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain_openai import ChatOpenAI\n",
    "\n",
    "chat = ChatOpenAI(temperature=0.0, model=\"gpt-3.5-turbo\")\n",
    "chat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Prompt template"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "template_string = \"\"\"Translate the text \\\n",
    "that is delimited by triple backticks \\\n",
    "into a style that is {style}. \\\n",
    "text: ```{text}```\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "\n",
    "prompt_template = ChatPromptTemplate.from_template(template_string)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PromptTemplate(input_variables=['style', 'text'], template='Translate the text that is delimited by triple backticks into a style that is {style}. text: ```{text}```\\n')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt_template.messages[0].prompt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_style = \"\"\"American english in calm and respectful tone\"\"\"\n",
    "customer_email = \"\"\"\n",
    "Arrr, I be fuming that me blender lid \\\n",
    "flew off and splattered me kitchen walls \\\n",
    "with smoothie! And to make matters worse,\\\n",
    "the warranty don't cover the cost of \\\n",
    "cleaning up me kitchen. I need yer help \\\n",
    "right now, matey!\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Call the LLM to translate to the style of the customer message\n",
    "\n",
    "customer_messages = prompt_template.format_messages(\n",
    "    style=customer_style, text=customer_email\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_response = chat.invoke(customer_messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I am really frustrated that my blender lid flew off and splattered my kitchen walls with smoothie! And to make matters worse, the warranty doesn't cover the cost of cleaning up my kitchen. I could really use your help right now, friend.\n"
     ]
    }
   ],
   "source": [
    "print(customer_response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_reply = \"\"\"Hey there customer, \\\n",
    "the warranty does not cover \\\n",
    "cleaning expenses for your kitchen \\\n",
    "because it's your fault that \\\n",
    "you misused your blender \\\n",
    "by forgetting to put the lid on before \\\n",
    "starting the blender. \\\n",
    "Tough luck! See ya!\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_style_pirate = \"\"\"\\\n",
    "a polite tone \\\n",
    "that speaks in English Pirate\\\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "service_messages = prompt_template.format_messages(\n",
    "    style=service_style_pirate, text=service_reply\n",
    ")\n",
    "\n",
    "service_response = chat.invoke(service_messages)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ahoy there, valued customer! Regrettably, the warranty be not coverin' the cost o' cleanin' yer galley due to yer own negligence in misusin' yer blender. Ye forgot to secure the lid afore startin' the blender, savvy? 'Tis a tough break, matey! Fare thee well!\n"
     ]
    }
   ],
   "source": [
    "print(service_response.content)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Output Parsers\n",
    "\n",
    "Let's start with defining how we would like the LLM output to look like:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'gift': False, 'delivery_days': 5, 'price_value': 'pretty affordable!'}"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "{\"gift\": False, \"delivery_days\": 5, \"price_value\": \"pretty affordable!\"}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "customer_review = \"\"\"\\\n",
    "This leaf blower is pretty amazing.  It has four settings:\\\n",
    "candle blower, gentle breeze, windy city, and tornado. \\\n",
    "It arrived in two days, just in time for my wife's \\\n",
    "anniversary present. \\\n",
    "I think my wife liked it so much she was speechless. \\\n",
    "So far I've been the only one using it, and I've been \\\n",
    "using it every other morning to clear the leaves on our lawn. \\\n",
    "It's slightly more expensive than the other leaf blowers \\\n",
    "out there, but I think it's worth it for the extra features.\n",
    "\"\"\"\n",
    "\n",
    "review_template = \"\"\"\\\n",
    "For the following text, extract the following information:\n",
    "\n",
    "gift: Was the item purchased as a gift for someone else? \\\n",
    "Answer True if yes, False if not or unknown.\n",
    "\n",
    "delivery_days: How many days did it take for the product \\\n",
    "to arrive? If this information is not found, output -1.\n",
    "\n",
    "price_value: Extract any sentences about the value or price,\\\n",
    "and output them as a comma separated Python list.\n",
    "\n",
    "Format the output as JSON with the following keys:\n",
    "gift\n",
    "delivery_days\n",
    "price_value\n",
    "\n",
    "text: {text}\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "input_variables=['text'] messages=[HumanMessagePromptTemplate(prompt=PromptTemplate(input_variables=['text'], template='For the following text, extract the following information:\\n\\ngift: Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.\\n\\ndelivery_days: How many days did it take for the product to arrive? If this information is not found, output -1.\\n\\nprice_value: Extract any sentences about the value or price,and output them as a comma separated Python list.\\n\\nFormat the output as JSON with the following keys:\\ngift\\ndelivery_days\\nprice_value\\n\\ntext: {text}\\n'))]\n"
     ]
    }
   ],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "\n",
    "prompt_template = ChatPromptTemplate.from_template(review_template)\n",
    "print(prompt_template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"gift\": true,\n",
      "    \"delivery_days\": 2,\n",
      "    \"price_value\": [\"It's slightly more expensive than the other leaf blowers out there\"]\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "messages = prompt_template.format_messages(text=customer_review)\n",
    "chat = ChatOpenAI(temperature=0.0, model=\"gpt-3.5-turbo\")\n",
    "response = chat.invoke(messages)\n",
    "print(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# You will get an error by running this line of code\n",
    "# because'gift' is not a dictionary\n",
    "# 'gift' is a string\n",
    "\n",
    "try:\n",
    "    gift = response.content.get(\"gift\")\n",
    "    print(gift)\n",
    "except Exception as e:\n",
    "    print(f\"Error: {e}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Parse the LLM output string into a Python dictionary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.output_parsers import ResponseSchema, StructuredOutputParser\n",
    "\n",
    "gift_schema = ResponseSchema(\n",
    "    name=\"gift\",\n",
    "    description=\"Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.\",\n",
    ")\n",
    "delivery_days_schema = ResponseSchema(\n",
    "    name=\"delivery_days\",\n",
    "    description=\"How many days did it take for the product to arrive? If this information is not found, output -1.\",\n",
    ")\n",
    "price_value_schema = ResponseSchema(\n",
    "    name=\"price_value\",\n",
    "    description=\"Extract any sentences about the value or price, and output them as a comma separated Python list.\",\n",
    ")\n",
    "\n",
    "response_schemas = [gift_schema, delivery_days_schema, price_value_schema]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "output_parser = StructuredOutputParser.from_response_schemas(response_schemas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "format_instructions = output_parser.get_format_instructions()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The output should be a markdown code snippet formatted in the following schema, including the leading and trailing \"```json\" and \"```\":\n",
      "\n",
      "```json\n",
      "{\n",
      "\t\"gift\": string  // Was the item purchased as a gift for someone else? Answer True if yes, False if not or unknown.\n",
      "\t\"delivery_days\": string  // How many days did it take for the product to arrive? If this information is not found, output -1.\n",
      "\t\"price_value\": string  // Extract any sentences about the value or price, and output them as a comma separated Python list.\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "print(format_instructions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "review_template_2 = \"\"\"\\\n",
    "For the following text, extract the following information:\n",
    "\n",
    "gift: Was the item purchased as a gift for someone else? \\\n",
    "Answer True if yes, False if not or unknown.\n",
    "\n",
    "delivery_days: How many days did it take for the product\\\n",
    "to arrive? If this information is not found, output -1.\n",
    "\n",
    "price_value: Extract any sentences about the value or price,\\\n",
    "and output them as a comma separated Python list.\n",
    "\n",
    "text: {text}\n",
    "\n",
    "{format_instructions}\n",
    "\"\"\"\n",
    "\n",
    "prompt = ChatPromptTemplate.from_template(template=review_template_2)\n",
    "\n",
    "messages = prompt.format_messages(\n",
    "    text=customer_review, format_instructions=format_instructions\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "```json\n",
      "{\n",
      "\t\"gift\": \"True\",\n",
      "\t\"delivery_days\": \"2\",\n",
      "\t\"price_value\": \"It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features.\"\n",
      "}\n",
      "```\n"
     ]
    }
   ],
   "source": [
    "response = chat.invoke(messages)\n",
    "print(response.content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "True\n",
      "2\n",
      "It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'gift': 'True',\n",
       " 'delivery_days': '2',\n",
       " 'price_value': \"It's slightly more expensive than the other leaf blowers out there, but I think it's worth it for the extra features.\"}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_dict = output_parser.parse(response.content)\n",
    "\n",
    "print(output_dict.get(\"gift\"))\n",
    "print(output_dict.get(\"delivery_days\"))\n",
    "print(output_dict.get(\"price_value\"))\n",
    "output_dict"
   ]
  }
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