chains.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1a305246-59d9-453e-9903-ca462611f1eb",
   "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.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Waterproof Phone Pcuch</td>\n",
       "      <td>I loved the waterproof sac</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pillows Insert</td>\n",
       "      <td>this is the best pillow filters on amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk Frother Handheld\\nm</td>\n",
       "      <td>I loved this product But they only seem to I</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.\n",
       "1    Waterproof Phone Pcuch                     I loved the waterproof sac\n",
       "2       Luxury Air Mattress      This mattress had a small hole in the top\n",
       "3            Pillows Insert      this is the best pillow filters on amazon\n",
       "4  Milk Frother Handheld\\nm   I loved this product But they only seem to I"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import openai\n",
    "import os\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv())\n",
    "import pandas as pd\n",
    "\n",
    "df = pd.read_csv('Data.csv')\n",
    "df.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "8f75bedd-2b00-466a-a00f-bc7c47f2f7a8",
   "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": 33,
   "id": "edc76aa3-1d1f-41d2-93a9-8297268817ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm = ChatOpenAI(temperature=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "27f2d3c9-9601-4b31-8d35-bae9538ee2a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Prompt template 1\n",
    "prompt = ChatPromptTemplate.from_template(\"what is the best name to describe a company that make {product}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "03ef9080-77ba-4406-a402-e4d5b889be47",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chain 1\n",
    "chain = LLMChain(llm=llm, prompt=prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "0a172ea1-18ce-407b-bcd9-b4146c5f5610",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1. Royal Comfort Linens\\n2. Majestic Bedding Co.\\n3. Crowned Sleep Essentials\\n4. Regal Dreams Bedding\\n5. Luxurious Queen Linens\\n6. Elite Serenity Bedding\\n7. Imperial Queen Bedding\\n8. Opulence Bed Linens\\n9. Grandeur Sleep Essentials\\n10. Prestige Queen Linens'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "product = \"Queen Size Sheet Set\"\n",
    "chain.run(product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b0ff950c-1c09-4ba5-8661-626fb69b24ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new SimpleSequentialChain chain...\u001b[0m\n",
      "\u001b[36;1m\u001b[1;3mRoyal Comfort Linens\u001b[0m\n",
      "\u001b[33;1m\u001b[1;3mRoyal Comfort Linens is a luxurious bedding company that offers high-quality and stylish linens for a truly regal sleep experience.\u001b[0m\n",
      "\n",
      "\u001b[1m> Finished chain.\u001b[0m\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Royal Comfort Linens is a luxurious bedding company that offers high-quality and stylish linens for a truly regal sleep experience.'"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from langchain.chains import SimpleSequentialChain\n",
    "# Prompt Template 2\n",
    "prompt_2 = ChatPromptTemplate.from_template(\"Write a 20 words description for the following company: {company_name}\")\n",
    "\n",
    "# chain 2\n",
    "chain_two = LLMChain(llm=llm, prompt=prompt_2)\n",
    "\n",
    "overall_simple_chain = SimpleSequentialChain(chains=[chain, chain_two], verbose=True)\n",
    "overall_simple_chain.run(product)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8ab602a8-9871-4881-b3eb-fbc1e9a90e08",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.chains import SequentialChain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "7101b29c-cc62-48ed-925c-962c9da6c40b",
   "metadata": {},
   "outputs": [],
   "source": [
    "llm_model = 'gpt-3.5-turbo'\n",
    "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": 40,
   "id": "cd8f164e-e91e-4fa5-a27a-40714aacdc32",
   "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",
    "                    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6a77ec8a-d5da-4889-9803-1676a03d046b",
   "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": 42,
   "id": "5fd5d216-74f1-4c73-9ba5-008a57398d65",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 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",
    "                     )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "6217843a-4244-48da-8622-0f753984a8f4",
   "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": 48,
   "id": "2c58c69a-e943-4257-a61c-579ca6eb8f4f",
   "metadata": {},
   "outputs": [
    {
     "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': ' this is the best pillow filters on amazon',\n",
       " 'English_Review': 'Este es el mejor filtro de almohadas en Amazon.',\n",
       " 'summary': 'This is the best pillow filter on Amazon.',\n",
       " 'followup_message': \"Response: Thank you for your review! We appreciate your feedback and we're glad to hear that you think our pillow filter is the best on Amazon. We strive to provide high-quality products to our customers, and your satisfaction is our top priority. If you have any further questions or need assistance, please don't hesitate to reach out.\"}"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "review = df.Review[3]\n",
    "overall_chain(review)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "c5f9c68b-020b-418a-842f-f2c32f958389",
   "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.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Waterproof Phone Pcuch</td>\n",
       "      <td>I loved the waterproof sac</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</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Pillows Insert</td>\n",
       "      <td>this is the best pillow filters on amazon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Milk Frother Handheld\\nm</td>\n",
       "      <td>I loved this product But they only seem to I</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.\n",
       "1    Waterproof Phone Pcuch                     I loved the waterproof sac\n",
       "2       Luxury Air Mattress      This mattress had a small hole in the top\n",
       "3            Pillows Insert      this is the best pillow filters on amazon\n",
       "4  Milk Frother Handheld\\nm   I loved this product But they only seem to I"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "27355bcf-2787-470d-a41a-ea5b5565a68a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0        Queen Size sheet Set\n",
       "1      Waterproof Phone Pcuch\n",
       "2         Luxury Air Mattress\n",
       "3              Pillows Insert\n",
       "4    Milk Frother Handheld\\nm\n",
       "Name: Product, dtype: object"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Product']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "8de4ae55-2cab-43fd-a2e9-542157919cbb",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 50,
   "id": "ec18c579-c130-490f-ad56-a2acd4f8bd60",
   "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": 51,
   "id": "b38acbf5-d22c-4951-b2e0-c2beed545474",
   "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\n",
    "\n",
    "llm = ChatOpenAI(temperature=0, model=llm_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "eecb95b3-38ed-4433-a918-8febfe1945d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "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": 53,
   "id": "228a3439-008d-47cd-ac10-fca1eccba51c",
   "metadata": {},
   "outputs": [],
   "source": [
    "default_prompt = ChatPromptTemplate.from_template(\"{input}\")\n",
    "default_chain = LLMChain(llm=llm, prompt=default_prompt)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "8b393708-04d1-40ab-a8ed-a1862d00b326",
   "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": 55,
   "id": "2f40b20c-89ed-4217-9176-60a9ab5d45d2",
   "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": 56,
   "id": "f89d7a92-3696-4fb9-890a-36f5bd6d3752",
   "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": 57,
   "id": "449208c8-2b58-4770-bb45-ae3c0778b432",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sushantgo/Downloads/conda/lib/python3.11/site-packages/langchain/chains/llm.py:280: UserWarning: The predict_and_parse method is deprecated, instead pass an output parser directly to LLMChain.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "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 an object that absorbs all incident radiation and reflects or transmits none. It is called \"black body\" because it absorbs all wavelengths of light, appearing black at room temperature. \\n\\nAccording to Planck\\'s law, black body radiation is characterized by a continuous spectrum of wavelengths and intensities, which depend on the temperature of the object. As the temperature increases, the peak intensity of the radiation shifts to shorter wavelengths, resulting in a change in color from red to orange, yellow, white, and eventually blue at very high temperatures.\\n\\nBlack body radiation is a fundamental concept in physics and has various applications, including understanding the behavior of stars, explaining the cosmic microwave background radiation, and developing technologies like incandescent light bulbs and thermal imaging devices.'"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"What is black body radiation?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "5c204f5e-5d20-4061-b08c-ad61999d3107",
   "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": [
       "\"Thank you for your kind words! As a mathematician, I am happy to help with any math questions, no matter how simple or complex they may be.\\n\\nNow, let's solve the problem at hand. The question is asking for the sum of 2 and 2. To find the answer, we can simply add the two numbers together:\\n\\n2 + 2 = 4\\n\\nTherefore, the sum of 2 and 2 is 4.\""
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.run(\"what is 2 + 2\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "60de9868-1821-414e-9564-3847c62dbef9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "\u001b[1m> Entering new MultiPromptChain chain...\u001b[0m\n",
      "biology: {'input': 'Why does every cell in our body contain DNA?'}"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "Received invalid destination chain name 'biology'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[59], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mchain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrun\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWhy does every cell in our body contain DNA?\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32m~/Downloads/conda/lib/python3.11/site-packages/langchain/chains/base.py:487\u001b[0m, in \u001b[0;36mChain.run\u001b[0;34m(self, callbacks, tags, metadata, *args, **kwargs)\u001b[0m\n\u001b[1;32m    485\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(args) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m    486\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m`run` supports only one positional argument.\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 487\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43margs\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtags\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtags\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mmetadata\u001b[49m\u001b[43m)\u001b[49m[\n\u001b[1;32m    488\u001b[0m         _output_key\n\u001b[1;32m    489\u001b[0m     ]\n\u001b[1;32m    491\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m kwargs \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m args:\n\u001b[1;32m    492\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m(kwargs, callbacks\u001b[38;5;241m=\u001b[39mcallbacks, tags\u001b[38;5;241m=\u001b[39mtags, metadata\u001b[38;5;241m=\u001b[39mmetadata)[\n\u001b[1;32m    493\u001b[0m         _output_key\n\u001b[1;32m    494\u001b[0m     ]\n",
      "File \u001b[0;32m~/Downloads/conda/lib/python3.11/site-packages/langchain/chains/base.py:292\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    291\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n\u001b[0;32m--> 292\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m e\n\u001b[1;32m    293\u001b[0m run_manager\u001b[38;5;241m.\u001b[39mon_chain_end(outputs)\n\u001b[1;32m    294\u001b[0m final_outputs: Dict[\u001b[38;5;28mstr\u001b[39m, Any] \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mprep_outputs(\n\u001b[1;32m    295\u001b[0m     inputs, outputs, return_only_outputs\n\u001b[1;32m    296\u001b[0m )\n",
      "File \u001b[0;32m~/Downloads/conda/lib/python3.11/site-packages/langchain/chains/base.py:286\u001b[0m, in \u001b[0;36mChain.__call__\u001b[0;34m(self, inputs, return_only_outputs, callbacks, tags, metadata, run_name, include_run_info)\u001b[0m\n\u001b[1;32m    279\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[1;32m    280\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    281\u001b[0m     inputs,\n\u001b[1;32m    282\u001b[0m     name\u001b[38;5;241m=\u001b[39mrun_name,\n\u001b[1;32m    283\u001b[0m )\n\u001b[1;32m    284\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m    285\u001b[0m     outputs \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m--> 286\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call\u001b[49m\u001b[43m(\u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    287\u001b[0m         \u001b[38;5;28;01mif\u001b[39;00m new_arg_supported\n\u001b[1;32m    288\u001b[0m         \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_call(inputs)\n\u001b[1;32m    289\u001b[0m     )\n\u001b[1;32m    290\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[1;32m    291\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[0;32m~/Downloads/conda/lib/python3.11/site-packages/langchain/chains/router/base.py:105\u001b[0m, in \u001b[0;36mMultiRouteChain._call\u001b[0;34m(self, inputs, run_manager)\u001b[0m\n\u001b[1;32m    103\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdefault_chain(route\u001b[38;5;241m.\u001b[39mnext_inputs, callbacks\u001b[38;5;241m=\u001b[39mcallbacks)\n\u001b[1;32m    104\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m--> 105\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m    106\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mReceived invalid destination chain name \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mroute\u001b[38;5;241m.\u001b[39mdestination\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    107\u001b[0m     )\n",
      "\u001b[0;31mValueError\u001b[0m: Received invalid destination chain name 'biology'"
     ]
    }
   ],
   "source": [
    "chain.run(\"Why does every cell in our body contain DNA?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9cbed99-9f90-4065-9b8f-a12de403c15a",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}