llmChains.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# account for deprecation of LLM model\n",
    "import datetime\n",
    "# Get the current date\n",
    "current_date = datetime.datetime.now().date()\n",
    "\n",
    "# Define the date after which the model should be set to \"gpt-3.5-turbo\"\n",
    "target_date = datetime.date(2024, 6, 12)\n",
    "\n",
    "# Set the model variable based on the current date\n",
    "if current_date > target_date:\n",
    "    llm_model = \"gpt-3.5-turbo\"\n",
    "else:\n",
    "    llm_model = \"gpt-3.5-turbo-0301\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pandas\n",
      "  Using cached pandas-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (19 kB)\n",
      "Requirement already satisfied: numpy<2,>=1.22.4 in ./.venv/lib/python3.10/site-packages (from pandas) (1.26.4)\n",
      "Requirement already satisfied: python-dateutil>=2.8.2 in ./.venv/lib/python3.10/site-packages (from pandas) (2.8.2)\n",
      "Collecting pytz>=2020.1 (from pandas)\n",
      "  Using cached pytz-2024.1-py2.py3-none-any.whl.metadata (22 kB)\n",
      "Collecting tzdata>=2022.7 (from pandas)\n",
      "  Using cached tzdata-2024.1-py2.py3-none-any.whl.metadata (1.4 kB)\n",
      "Requirement already satisfied: six>=1.5 in ./.venv/lib/python3.10/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n",
      "Using cached pandas-2.2.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.0 MB)\n",
      "Using cached pytz-2024.1-py2.py3-none-any.whl (505 kB)\n",
      "Using cached tzdata-2024.1-py2.py3-none-any.whl (345 kB)\n",
      "Installing collected packages: pytz, tzdata, pandas\n",
      "Successfully installed pandas-2.2.0 pytz-2024.1 tzdata-2024.1\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('Data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Product</th>\n",
       "      <th>Review</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Queen Size Sheet Set</td>\n",
       "      <td>I ordered a king size set. My only criticism w...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Waterproof Phone Pouch</td>\n",
       "      <td>I loved the waterproof sac, although the openi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Luxury Air Mattress</td>\n",
       "      <td>This mattress had a small hole in the top of i...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pillows Insert</td>\n",
       "      <td>This is the best throw pillow fillers on Amazo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk Frother Handheld\\n</td>\n",
       "      <td>I loved this product. But they only seem to l...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Product                                             Review\n",
       "0     Queen Size Sheet Set  I ordered a king size set. My only criticism w...\n",
       "1   Waterproof Phone Pouch  I loved the waterproof sac, although the openi...\n",
       "2      Luxury Air Mattress  This mattress had a small hole in the top of i...\n",
       "3           Pillows Insert  This is the best throw pillow fillers on Amazo...\n",
       "4  Milk Frother Handheld\\n   I loved this product. But they only seem to l..."
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chains import LLMChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/sideProjects/LangChain_explore/.venv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The class `langchain_community.chat_models.openai.ChatOpenAI` was deprecated in langchain-community 0.0.10 and will be removed in 0.2.0. An updated version of the class exists in the langchain-openai package and should be used instead. To use it run `pip install -U langchain-openai` and import as `from langchain_openai import ChatOpenAI`.\n",
      "  warn_deprecated(\n"
     ]
    }
   ],
   "source": [
    "llm = ChatOpenAI(temperature=0.9, model=llm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/sideProjects/LangChain_explore/.venv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `run` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\"Royal Rest Bedding Co.\"'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"What is the best name to describe \\\n",
    "    a company that makes {product}?\"\n",
    ")\n",
    "chain = LLMChain(llm=llm, prompt=prompt)\n",
    "product = \"Queen Size Sheet Set\"\n",
    "chain.run(product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#simple sequential chain\n",
    "from langchain.chains import SimpleSequentialChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0.9, model=llm_model)\n",
    "\n",
    "# prompt template 1\n",
    "first_prompt = ChatPromptTemplate.from_template(\n",
    "    \"What is the best name to describe \\\n",
    "    a company that makes {product}?\"\n",
    ")\n",
    "\n",
    "# Chain 1\n",
    "chain_one = LLMChain(llm=llm, prompt=first_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prompt template 2\n",
    "second_prompt = ChatPromptTemplate.from_template(\n",
    "    \"Write a 20 words description for the following \\\n",
    "    company:{company_name}\"\n",
    ")\n",
    "# chain 2\n",
    "chain_two = LLMChain(llm=llm, prompt=second_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "overall_simple_chain = SimpleSequentialChain(chains=[chain_one, chain_two],\n",
    "                                             verbose=True\n",
    "                                            )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3mQueenly Linens\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3mQueenly Linens is a luxurious bedding and bath linen brand known for its high-quality, elegant, and comfortable products.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Queenly Linens is a luxurious bedding and bath linen brand known for its high-quality, elegant, and comfortable products.'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "overall_simple_chain.run(product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#sequential chain\n",
    "from langchain.chains import SequentialChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0.9, model=llm_model)\n",
    "\n",
    "# prompt template 1: translate to english\n",
    "first_prompt = ChatPromptTemplate.from_template(\n",
    "    \"Translate the following review to english:\"\n",
    "    \"\\n\\n{Review}\"\n",
    ")\n",
    "# chain 1: input= Review and output= English_Review\n",
    "chain_one = LLMChain(llm=llm, prompt=first_prompt, \n",
    "                     output_key=\"English_Review\"\n",
    "                    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "second_prompt = ChatPromptTemplate.from_template(\n",
    "    \"Can you summarize the following review in 1 sentence:\"\n",
    "    \"\\n\\n{English_Review}\"\n",
    ")\n",
    "# chain 2: input= English_Review and output= summary\n",
    "chain_two = LLMChain(llm=llm, prompt=second_prompt, \n",
    "                     output_key=\"summary\"\n",
    "                    )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# prompt template 3: translate to english\n",
    "third_prompt = ChatPromptTemplate.from_template(\n",
    "    \"What language is the following review:\\n\\n{Review}\"\n",
    ")\n",
    "# chain 3: input= Review and output= language\n",
    "chain_three = LLMChain(llm=llm, prompt=third_prompt,\n",
    "                       output_key=\"language\"\n",
    "                      )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# prompt template 4: follow up message\n",
    "fourth_prompt = ChatPromptTemplate.from_template(\n",
    "    \"Write a follow up response to the following \"\n",
    "    \"summary in the specified language:\"\n",
    "    \"\\n\\nSummary: {summary}\\n\\nLanguage: {language}\"\n",
    ")\n",
    "# chain 4: input= summary, language and output= followup_message\n",
    "chain_four = LLMChain(llm=llm, prompt=fourth_prompt,\n",
    "                      output_key=\"followup_message\"\n",
    "                     )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# overall_chain: input= Review \n",
    "# and output= English_Review,summary, followup_message\n",
    "overall_chain = SequentialChain(\n",
    "    chains=[chain_one, chain_two, chain_three, chain_four],\n",
    "    input_variables=[\"Review\"],\n",
    "    output_variables=[\"English_Review\", \"summary\",\"followup_message\"],\n",
    "    verbose=True\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/sideProjects/LangChain_explore/.venv/lib/python3.10/site-packages/langchain_core/_api/deprecation.py:117: LangChainDeprecationWarning: The function `__call__` was deprecated in LangChain 0.1.0 and will be removed in 0.2.0. Use invoke instead.\n",
      "  warn_deprecated(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SequentialChain chain...\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'Review': \"Je trouve le goût médiocre. La mousse ne tient pas, c'est bizarre. J'achète les mêmes dans le commerce et le goût est bien meilleur...\\nVieux lot ou contrefaçon !?\",\n",
       " 'English_Review': \"I find the taste mediocre. The foam doesn't hold, it's weird. I buy the same ones in stores and the taste is much better... Old batch or counterfeit!?\",\n",
       " 'summary': 'The reviewer found the taste of the product to be mediocre, and suspects that it may be an old batch or counterfeit due to the foam not holding.',\n",
       " 'followup_message': \"Le critique a trouvé le goût du produit moyen et soupçonne qu'il pourrait s'agir d'un lot ancien ou contrefait en raison de la mousse qui ne tient pas.\"}"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review = df.Review[5]\n",
    "overall_chain(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "#router chain\n",
    "physics_template = \"\"\"You are a very smart physics professor. \\\n",
    "You are great at answering questions about physics in a concise\\\n",
    "and easy to understand manner. \\\n",
    "When you don't know the answer to a question you admit\\\n",
    "that you don't know.\n",
    "\n",
    "Here is a question:\n",
    "{input}\"\"\"\n",
    "\n",
    "\n",
    "math_template = \"\"\"You are a very good mathematician. \\\n",
    "You are great at answering math questions. \\\n",
    "You are so good because you are able to break down \\\n",
    "hard problems into their component parts, \n",
    "answer the component parts, and then put them together\\\n",
    "to answer the broader question.\n",
    "\n",
    "Here is a question:\n",
    "{input}\"\"\"\n",
    "\n",
    "history_template = \"\"\"You are a very good historian. \\\n",
    "You have an excellent knowledge of and understanding of people,\\\n",
    "events and contexts from a range of historical periods. \\\n",
    "You have the ability to think, reflect, debate, discuss and \\\n",
    "evaluate the past. You have a respect for historical evidence\\\n",
    "and the ability to make use of it to support your explanations \\\n",
    "and judgements.\n",
    "\n",
    "Here is a question:\n",
    "{input}\"\"\"\n",
    "\n",
    "\n",
    "computerscience_template = \"\"\" You are a successful computer scientist.\\\n",
    "You have a passion for creativity, collaboration,\\\n",
    "forward-thinking, confidence, strong problem-solving capabilities,\\\n",
    "understanding of theories and algorithms, and excellent communication \\\n",
    "skills. You are great at answering coding questions. \\\n",
    "You are so good because you know how to solve a problem by \\\n",
    "describing the solution in imperative steps \\\n",
    "that a machine can easily interpret and you know how to \\\n",
    "choose a solution that has a good balance between \\\n",
    "time complexity and space complexity. \n",
    "\n",
    "Here is a question:\n",
    "{input}\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt_infos = [\n",
    "    {\n",
    "        \"name\": \"physics\", \n",
    "        \"description\": \"Good for answering questions about physics\", \n",
    "        \"prompt_template\": physics_template\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"math\", \n",
    "        \"description\": \"Good for answering math questions\", \n",
    "        \"prompt_template\": math_template\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"History\", \n",
    "        \"description\": \"Good for answering history questions\", \n",
    "        \"prompt_template\": history_template\n",
    "    },\n",
    "    {\n",
    "        \"name\": \"computer science\", \n",
    "        \"description\": \"Good for answering computer science questions\", \n",
    "        \"prompt_template\": computerscience_template\n",
    "    }\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains.router import MultiPromptChain\n",
    "from langchain.chains.router.llm_router import LLMRouterChain,RouterOutputParser\n",
    "from langchain.prompts import PromptTemplate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0, model=llm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "destination_chains = {}\n",
    "for p_info in prompt_infos:\n",
    "    name = p_info[\"name\"]\n",
    "    prompt_template = p_info[\"prompt_template\"]\n",
    "    prompt = ChatPromptTemplate.from_template(template=prompt_template)\n",
    "    chain = LLMChain(llm=llm, prompt=prompt)\n",
    "    destination_chains[name] = chain  \n",
    "    \n",
    "destinations = [f\"{p['name']}: {p['description']}\" for p in prompt_infos]\n",
    "destinations_str = \"\\n\".join(destinations)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "default_prompt = ChatPromptTemplate.from_template(\"{input}\")\n",
    "default_chain = LLMChain(llm=llm, prompt=default_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "MULTI_PROMPT_ROUTER_TEMPLATE = \"\"\"Given a raw text input to a \\\n",
    "language model select the model prompt best suited for the input. \\\n",
    "You will be given the names of the available prompts and a \\\n",
    "description of what the prompt is best suited for. \\\n",
    "You may also revise the original input if you think that revising\\\n",
    "it will ultimately lead to a better response from the language model.\n",
    "\n",
    "<< FORMATTING >>\n",
    "Return a markdown code snippet with a JSON object formatted to look like:\n",
    "```json\n",
    "{{{{\n",
    "    \"destination\": string \\ name of the prompt to use or \"DEFAULT\"\n",
    "    \"next_inputs\": string \\ a potentially modified version of the original input\n",
    "}}}}\n",
    "```\n",
    "\n",
    "REMEMBER: \"destination\" MUST be one of the candidate prompt \\\n",
    "names specified below OR it can be \"DEFAULT\" if the input is not\\\n",
    "well suited for any of the candidate prompts.\n",
    "REMEMBER: \"next_inputs\" can just be the original input \\\n",
    "if you don't think any modifications are needed.\n",
    "\n",
    "<< CANDIDATE PROMPTS >>\n",
    "{destinations}\n",
    "\n",
    "<< INPUT >>\n",
    "{{input}}\n",
    "\n",
    "<< OUTPUT (remember to include the ```json)>>\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "router_template = MULTI_PROMPT_ROUTER_TEMPLATE.format(\n",
    "    destinations=destinations_str\n",
    ")\n",
    "router_prompt = PromptTemplate(\n",
    "    template=router_template,\n",
    "    input_variables=[\"input\"],\n",
    "    output_parser=RouterOutputParser(),\n",
    ")\n",
    "\n",
    "router_chain = LLMRouterChain.from_llm(llm, router_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = MultiPromptChain(router_chain=router_chain, \n",
    "                         destination_chains=destination_chains, \n",
    "                         default_chain=default_chain, verbose=True\n",
    "                        )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
      "physics: {'input': 'What is black body radiation?'}\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "\"Black body radiation refers to the electromagnetic radiation emitted by a perfect black body, which is an object that absorbs all radiation that falls on it and emits radiation at all wavelengths. The radiation emitted by a black body depends only on its temperature and follows a specific distribution known as Planck's law. This type of radiation is important in understanding the behavior of stars, as well as in the development of technologies such as incandescent light bulbs and infrared cameras.\""
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"What is black body radiation?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
      "math: {'input': 'what is 2 + 2'}\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'As an AI language model, I can answer this question easily. The answer to 2 + 2 is 4.'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"what is 2 + 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
      "None: {'input': 'Why does every cell in our body contain DNA?'}\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Every cell in our body contains DNA because DNA carries the genetic information that determines the characteristics and functions of the cell. DNA contains the instructions for the synthesis of proteins, which are essential for the structure and function of cells. Additionally, DNA is responsible for the transmission of genetic information from one generation to the next. Therefore, every cell in our body needs DNA to carry out its specific functions and to maintain the integrity of the organism as a whole.'"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"Why does every cell in our body contain DNA?\")"
   ]
  }
 ],
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