L2-lcel-student.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "fc925562",
   "metadata": {},
   "source": [
    "# LangChain Expression Language (LCEL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "044db1b7-c18c-45d2-be7a-29f027c901e2",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import openai\n",
    "\n",
    "from dotenv import load_dotenv, find_dotenv\n",
    "_ = load_dotenv(find_dotenv()) # read local .env file\n",
    "openai.api_key = os.environ['OPENAI_API_KEY']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2de9d2bd-18de-44d5-81ac-5918e2106367",
   "metadata": {},
   "outputs": [],
   "source": [
    "#!pip install pydantic==1.10.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fd55c0a0-ca4e-4311-a33c-fcebeb7d8b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain.chat_models import ChatOpenAI\n",
    "from langchain.schema.output_parser import StrOutputParser"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e99c432b-b2c9-497b-9912-c9ca4c1e3740",
   "metadata": {},
   "source": [
    "## Simple Chain"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6a0be20d-0e00-478c-a844-017cad13af22",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"tell me a short joke about {topic}\"\n",
    ")\n",
    "model = ChatOpenAI()\n",
    "output_parser = StrOutputParser()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aedf1c1e-b697-47ce-9d81-eaec9192243b",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = prompt | model | output_parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6df32028-d35f-4392-bb15-ddeec9ee09b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes? \\n\\nBecause they have bear feet!\""
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"topic\": \"bears\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8ba33b5-2ae9-44d7-b023-18c903af571a",
   "metadata": {},
   "source": [
    "## More complex chain\n",
    "\n",
    "And Runnable Map to supply user-provided inputs to the prompt."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "d036bb8e-8ca7-4dbd-8103-f50a3c8c3af9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import OpenAIEmbeddings\n",
    "from langchain.vectorstores import DocArrayInMemorySearch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "8955cff7-f1a2-4f94-ab5b-fcdda0859702",
   "metadata": {},
   "outputs": [],
   "source": [
    "vectorstore = DocArrayInMemorySearch.from_texts(\n",
    "    [\"harrison worked at kensho\", \"bears like to eat honey\"],\n",
    "    embedding=OpenAIEmbeddings()\n",
    ")\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "2df87934-1697-405c-b460-5e9bfd16c792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='harrison worked at kensho'),\n",
       " Document(page_content='bears like to eat honey')]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"where did harrison work?\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "871cb26b-97b3-4f63-8bb3-523d3e6d117b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Document(page_content='bears like to eat honey'),\n",
       " Document(page_content='harrison worked at kensho')]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "retriever.get_relevant_documents(\"what do bears like to eat\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "127a7fb6-5821-4934-ab56-9e3300516c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "template = \"\"\"Answer the question based only on the following context:\n",
    "{context}\n",
    "\n",
    "Question: {question}\n",
    "\"\"\"\n",
    "prompt = ChatPromptTemplate.from_template(template)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4ec01c56-731c-4e4f-a5f6-493fba953db0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.schema.runnable import RunnableMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a9ca6506-826f-4420-8f19-25dd4dbbc1dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "chain = RunnableMap({\n",
    "    \"context\": lambda x: retriever.get_relevant_documents(x[\"question\"]),\n",
    "    \"question\": lambda x: x[\"question\"]\n",
    "}) | prompt | model | output_parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "707d1319-8840-4ed5-b4a4-a2a128799db6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Harrison worked at Kensho.'"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"question\": \"where did harrison work?\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "05ec3727-4284-417e-9e23-eec0682eb002",
   "metadata": {},
   "outputs": [],
   "source": [
    "inputs = RunnableMap({\n",
    "    \"context\": lambda x: retriever.get_relevant_documents(x[\"question\"]),\n",
    "    \"question\": lambda x: x[\"question\"]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4216c7ab-6d1b-4f2a-98dc-5d2ace23e3c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'context': [Document(page_content='harrison worked at kensho'),\n",
       "  Document(page_content='bears like to eat honey')],\n",
       " 'question': 'where did harrison work?'}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inputs.invoke({\"question\": \"where did harrison work?\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eec59c3b-33e7-437f-9b8b-b4652bc3b863",
   "metadata": {},
   "source": [
    "## Bind\n",
    "\n",
    "and OpenAI Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f3efed3b-6d4c-42a4-9692-0cc4596f530b",
   "metadata": {},
   "outputs": [],
   "source": [
    "functions = [\n",
    "    {\n",
    "      \"name\": \"weather_search\",\n",
    "      \"description\": \"Search for weather given an airport code\",\n",
    "      \"parameters\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          \"airport_code\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The airport code to get the weather for\"\n",
    "          },\n",
    "        },\n",
    "        \"required\": [\"airport_code\"]\n",
    "      }\n",
    "    }\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "f8be4721-91d2-4ae6-8fdb-e91dc6ac1bc5",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_messages(\n",
    "    [\n",
    "        (\"human\", \"{input}\")\n",
    "    ]\n",
    ")\n",
    "model = ChatOpenAI(temperature=0).bind(functions=functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "e61b095d-9934-41b8-a794-a9dd57e9c733",
   "metadata": {},
   "outputs": [],
   "source": [
    "runnable = prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a638efeb-b5ce-4aa4-8377-3e86597a03ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'weather_search', 'arguments': '{\\n  \"airport_code\": \"SFO\"\\n}'}})"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runnable.invoke({\"input\": \"what is the weather in sf\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "3a22faf5-ea24-48d2-b028-03733b548225",
   "metadata": {},
   "outputs": [],
   "source": [
    "functions = [\n",
    "    {\n",
    "      \"name\": \"weather_search\",\n",
    "      \"description\": \"Search for weather given an airport code\",\n",
    "      \"parameters\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          \"airport_code\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The airport code to get the weather for\"\n",
    "          },\n",
    "        },\n",
    "        \"required\": [\"airport_code\"]\n",
    "      }\n",
    "    },\n",
    "        {\n",
    "      \"name\": \"sports_search\",\n",
    "      \"description\": \"Search for news of recent sport events\",\n",
    "      \"parameters\": {\n",
    "        \"type\": \"object\",\n",
    "        \"properties\": {\n",
    "          \"team_name\": {\n",
    "            \"type\": \"string\",\n",
    "            \"description\": \"The sports team to search for\"\n",
    "          },\n",
    "        },\n",
    "        \"required\": [\"team_name\"]\n",
    "      }\n",
    "    }\n",
    "  ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "eb43b030-459f-47e8-a27d-96c2d70cdfef",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = model.bind(functions=functions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "3ff03e0d-d6c6-4b47-815e-d7ea5b248567",
   "metadata": {},
   "outputs": [],
   "source": [
    "runnable = prompt | model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "03855fa3-5e2f-4ab2-aba0-c2cd5423239e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AIMessage(content='', additional_kwargs={'function_call': {'name': 'sports_search', 'arguments': '{\\n  \"team_name\": \"patriots\"\\n}'}})"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "runnable.invoke({\"input\": \"how did the patriots do yesterday?\"})"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bebc0c55-48c2-4105-90ec-7297553b8e6a",
   "metadata": {},
   "source": [
    "## Fallbacks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "aa0b1ea2-7aef-4449-a553-426cb8c5aa30",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.llms import OpenAI\n",
    "import json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "17f90857-5625-4dba-bf3f-99432bd7f971",
   "metadata": {},
   "outputs": [],
   "source": [
    "simple_model = OpenAI(\n",
    "    temperature=0, \n",
    "    max_tokens=1000, \n",
    "    model=\"text-davinci-001\"\n",
    ")\n",
    "simple_chain = simple_model | json.loads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "441928c5-8712-45c5-bfdf-6f51634198a7",
   "metadata": {},
   "outputs": [],
   "source": [
    "challenge = \"write three poems in a json blob, where each poem is a json blob of a title, author, and first line\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "0739e85f-8497-4471-8ec9-17e958d80771",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n\\n[\"The Waste Land\",\"T.S. Eliot\",\"April is the cruelest month, breeding lilacs out of the dead land\"]\\n\\n[\"The Raven\",\"Edgar Allan Poe\",\"Once upon a midnight dreary, while I pondered, weak and weary\"]\\n\\n[\"Ode to a Nightingale\",\"John Keats\",\"Thou still unravish\\'d bride of quietness, Thou foster-child of silence and slow time\"]'"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "simple_model.invoke(challenge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3a20d15c-dc8a-4b6d-a423-a5f814425219",
   "metadata": {},
   "source": [
    "Note: The next line is expected to fail."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2a5e8492-0927-4a3a-b939-947826246330",
   "metadata": {},
   "outputs": [
    {
     "ename": "JSONDecodeError",
     "evalue": "Extra data: line 5 column 1 (char 103)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[29], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43msimple_chain\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\u001b[43mchallenge\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\base.py:1153\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[1;34m(self, input, config)\u001b[0m\n\u001b[0;32m   1151\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m   1152\u001b[0m     \u001b[38;5;28;01mfor\u001b[39;00m i, step \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps):\n\u001b[1;32m-> 1153\u001b[0m         \u001b[38;5;28minput\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[43mstep\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43minvoke\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1154\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1155\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;66;43;03m# mark each step as a child run\u001b[39;49;00m\n\u001b[0;32m   1156\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   1157\u001b[0m \u001b[43m                \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrun_manager\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_child\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mseq:step:\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43mi\u001b[49m\u001b[38;5;241;43m+\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1158\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   1159\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1160\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[0;32m   1161\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",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\base.py:2231\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[1;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[0;32m   2229\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[0;32m   2230\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfunc\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 2231\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_with_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m   2232\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_invoke\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2233\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2234\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_config\u001b[49m\u001b[43m(\u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m   2235\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2236\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   2237\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[0;32m   2238\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot invoke a coroutine function synchronously.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2239\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   2240\u001b[0m     )\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\base.py:668\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[1;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[0;32m    661\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[0;32m    662\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[0;32m    663\u001b[0m     \u001b[38;5;28minput\u001b[39m,\n\u001b[0;32m    664\u001b[0m     run_type\u001b[38;5;241m=\u001b[39mrun_type,\n\u001b[0;32m    665\u001b[0m     name\u001b[38;5;241m=\u001b[39mconfig\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_name\u001b[39m\u001b[38;5;124m\"\u001b[39m),\n\u001b[0;32m    666\u001b[0m )\n\u001b[0;32m    667\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m--> 668\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    669\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[0;32m    670\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    671\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[0;32m    672\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\config.py:259\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[1;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[0;32m    258\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[1;32m--> 259\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\base.py:2164\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[1;34m(self, input, run_manager, config)\u001b[0m\n\u001b[0;32m   2158\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_invoke\u001b[39m(\n\u001b[0;32m   2159\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[0;32m   2160\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[0;32m   2161\u001b[0m     run_manager: CallbackManagerForChainRun,\n\u001b[0;32m   2162\u001b[0m     config: RunnableConfig,\n\u001b[0;32m   2163\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[1;32m-> 2164\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrun_manager\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2165\u001b[0m     \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[0;32m   2166\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\site-packages\\langchain\\schema\\runnable\\config.py:259\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[1;34m(func, input, config, run_manager, **kwargs)\u001b[0m\n\u001b[0;32m    257\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m run_manager \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m accepts_run_manager(func):\n\u001b[0;32m    258\u001b[0m     kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrun_manager\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m run_manager\n\u001b[1;32m--> 259\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\json\\__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[1;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[0;32m    341\u001b[0m     s \u001b[38;5;241m=\u001b[39m s\u001b[38;5;241m.\u001b[39mdecode(detect_encoding(s), \u001b[38;5;124m'\u001b[39m\u001b[38;5;124msurrogatepass\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m    343\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    344\u001b[0m         parse_int \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m parse_float \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m\n\u001b[0;32m    345\u001b[0m         parse_constant \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m object_pairs_hook \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m kw):\n\u001b[1;32m--> 346\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_default_decoder\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdecode\u001b[49m\u001b[43m(\u001b[49m\u001b[43ms\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    347\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mcls\u001b[39m \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m    348\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
      "File \u001b[1;32m~\\.pyenv\\pyenv-win\\versions\\3.11.3\\Lib\\json\\decoder.py:340\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[1;34m(self, s, _w)\u001b[0m\n\u001b[0;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[0;32m    339\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m end \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(s):\n\u001b[1;32m--> 340\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m JSONDecodeError(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mExtra data\u001b[39m\u001b[38;5;124m\"\u001b[39m, s, end)\n\u001b[0;32m    341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n",
      "\u001b[1;31mJSONDecodeError\u001b[0m: Extra data: line 5 column 1 (char 103)"
     ]
    }
   ],
   "source": [
    "simple_chain.invoke(challenge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6814143b-4a35-4d29-bd32-ba461274bcbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = ChatOpenAI(temperature=0)\n",
    "chain = model | StrOutputParser() | json.loads"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "f55f04cf-0217-4106-b41f-0e0661d12c27",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'poem1': {'title': 'Whispers of the Wind',\n",
       "  'author': 'Emily Rivers',\n",
       "  'first_line': 'Softly it comes, the whisper of the wind'},\n",
       " 'poem2': {'title': 'Silent Serenade',\n",
       "  'author': 'Jacob Moore',\n",
       "  'first_line': 'In the stillness of night, a silent serenade'},\n",
       " 'poem3': {'title': 'Dancing Shadows',\n",
       "  'author': 'Sophia Anderson',\n",
       "  'first_line': 'Shadows dance upon the moonlit floor'}}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke(challenge)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "b5d3f035-b18d-4cba-854d-a43ef8554e48",
   "metadata": {},
   "outputs": [],
   "source": [
    "final_chain = simple_chain.with_fallbacks([chain])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a09fe6a-548c-412d-9468-9efe2b49f7c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'poem1': {'title': 'Whispers of the Wind',\n",
       "  'author': 'Emily Rivers',\n",
       "  'first_line': 'Softly it comes, the whisper of the wind'},\n",
       " 'poem2': {'title': 'Silent Serenade',\n",
       "  'author': 'Jacob Moore',\n",
       "  'first_line': 'In the stillness of night, a silent serenade'},\n",
       " 'poem3': {'title': 'Dancing Shadows',\n",
       "  'author': 'Sophia Anderson',\n",
       "  'first_line': 'Shadows dance upon the walls, a secret ballet'}}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_chain.invoke(challenge)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3fcfdda0-3db2-4073-a647-f2d62c460349",
   "metadata": {},
   "source": [
    "## Interface"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "33b3b27f-b5a0-4db5-a1b0-20754437a47e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prompt = ChatPromptTemplate.from_template(\n",
    "    \"Tell me a short joke about {topic}\"\n",
    ")\n",
    "model = ChatOpenAI()\n",
    "output_parser = StrOutputParser()\n",
    "\n",
    "chain = prompt | model | output_parser"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "48c8cabf-ea55-4448-b070-3ec22942c559",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes? \\n\\nBecause they have bear feet!\""
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.invoke({\"topic\": \"bears\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "cc58bdb4-a896-46ba-90c4-1b333245229a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\",\n",
       " 'Why don\\'t frogs like using smartphones?\\n\\nBecause they already have plenty of \"webbed\" feet!']"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"frogs\"}])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "8a069d61-0a67-4368-b7c4-367262267bb8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Why\n",
      " don\n",
      "'t\n",
      " bears\n",
      " wear\n",
      " shoes\n",
      "?\n",
      " \n",
      "\n",
      "\n",
      "Because\n",
      " they\n",
      " have\n",
      " bear\n",
      " feet\n",
      "!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for t in chain.stream({\"topic\": \"bears\"}):\n",
    "    print(t)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "2315b43f-c7e1-4f7b-9595-4cabdc019dea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"Why don't bears wear shoes?\\n\\nBecause they have bear feet!\""
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response = await chain.ainvoke({\"topic\": \"bears\"})\n",
    "response"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3082379-f75e-4a74-bee5-c18ddc5ac4dc",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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