02-LCEL.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/JSJeong-me/GPT-Insights/blob/main/02-LCEL.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "fc925562",
      "metadata": {
        "id": "fc925562"
      },
      "source": [
        "# LangChain Expression Language (LCEL)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "044db1b7-c18c-45d2-be7a-29f027c901e2",
      "metadata": {
        "height": 116,
        "tags": [],
        "id": "044db1b7-c18c-45d2-be7a-29f027c901e2"
      },
      "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": null,
      "id": "2de9d2bd-18de-44d5-81ac-5918e2106367",
      "metadata": {
        "height": 31,
        "id": "2de9d2bd-18de-44d5-81ac-5918e2106367"
      },
      "outputs": [],
      "source": [
        "#!pip install pydantic==1.10.8"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "fd55c0a0-ca4e-4311-a33c-fcebeb7d8b1e",
      "metadata": {
        "height": 65,
        "id": "fd55c0a0-ca4e-4311-a33c-fcebeb7d8b1e"
      },
      "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": {
        "id": "e99c432b-b2c9-497b-9912-c9ca4c1e3740"
      },
      "source": [
        "## Simple Chain"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6a0be20d-0e00-478c-a844-017cad13af22",
      "metadata": {
        "height": 99,
        "id": "6a0be20d-0e00-478c-a844-017cad13af22"
      },
      "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": null,
      "id": "aedf1c1e-b697-47ce-9d81-eaec9192243b",
      "metadata": {
        "height": 30,
        "id": "aedf1c1e-b697-47ce-9d81-eaec9192243b"
      },
      "outputs": [],
      "source": [
        "chain = prompt | model | output_parser"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6df32028-d35f-4392-bb15-ddeec9ee09b5",
      "metadata": {
        "height": 30,
        "id": "6df32028-d35f-4392-bb15-ddeec9ee09b5",
        "outputId": "1c9c500e-4a1d-4125-8484-16c18fc38d6b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'Why do bears have hairy coats? \\n\\nFur protection!'"
            ]
          },
          "execution_count": 6,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "chain.invoke({\"topic\": \"bears\"})"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "d8ba33b5-2ae9-44d7-b023-18c903af571a",
      "metadata": {
        "id": "d8ba33b5-2ae9-44d7-b023-18c903af571a"
      },
      "source": [
        "## More complex chain\n",
        "\n",
        "And Runnable Map to supply user-provided inputs to the prompt."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d036bb8e-8ca7-4dbd-8103-f50a3c8c3af9",
      "metadata": {
        "height": 47,
        "id": "d036bb8e-8ca7-4dbd-8103-f50a3c8c3af9"
      },
      "outputs": [],
      "source": [
        "from langchain.embeddings import OpenAIEmbeddings\n",
        "from langchain.vectorstores import DocArrayInMemorySearch"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8955cff7-f1a2-4f94-ab5b-fcdda0859702",
      "metadata": {
        "height": 117,
        "id": "8955cff7-f1a2-4f94-ab5b-fcdda0859702"
      },
      "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": null,
      "id": "2df87934-1697-405c-b460-5e9bfd16c792",
      "metadata": {
        "height": 30,
        "id": "2df87934-1697-405c-b460-5e9bfd16c792",
        "outputId": "65a5e0d5-304c-4f30-c70a-569d7caba9c0"
      },
      "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": null,
      "id": "871cb26b-97b3-4f63-8bb3-523d3e6d117b",
      "metadata": {
        "height": 49,
        "id": "871cb26b-97b3-4f63-8bb3-523d3e6d117b",
        "outputId": "6d6bd757-44f9-48eb-a7b7-ce76690fec0a"
      },
      "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": null,
      "id": "127a7fb6-5821-4934-ab56-9e3300516c05",
      "metadata": {
        "height": 134,
        "id": "127a7fb6-5821-4934-ab56-9e3300516c05"
      },
      "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": null,
      "id": "4ec01c56-731c-4e4f-a5f6-493fba953db0",
      "metadata": {
        "height": 30,
        "id": "4ec01c56-731c-4e4f-a5f6-493fba953db0"
      },
      "outputs": [],
      "source": [
        "from langchain.schema.runnable import RunnableMap"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a9ca6506-826f-4420-8f19-25dd4dbbc1dc",
      "metadata": {
        "height": 100,
        "id": "a9ca6506-826f-4420-8f19-25dd4dbbc1dc"
      },
      "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": null,
      "id": "707d1319-8840-4ed5-b4a4-a2a128799db6",
      "metadata": {
        "height": 30,
        "id": "707d1319-8840-4ed5-b4a4-a2a128799db6",
        "outputId": "88f15062-178b-46ff-feff-123570ae2caf"
      },
      "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": null,
      "id": "05ec3727-4284-417e-9e23-eec0682eb002",
      "metadata": {
        "height": 100,
        "id": "05ec3727-4284-417e-9e23-eec0682eb002"
      },
      "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": null,
      "id": "4216c7ab-6d1b-4f2a-98dc-5d2ace23e3c2",
      "metadata": {
        "height": 30,
        "id": "4216c7ab-6d1b-4f2a-98dc-5d2ace23e3c2",
        "outputId": "68fb1aff-e3c3-46cd-d861-11c0b6292b11"
      },
      "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": {
        "id": "eec59c3b-33e7-437f-9b8b-b4652bc3b863"
      },
      "source": [
        "## Bind\n",
        "\n",
        "and OpenAI Functions"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3efed3b-6d4c-42a4-9692-0cc4596f530b",
      "metadata": {
        "height": 304,
        "id": "f3efed3b-6d4c-42a4-9692-0cc4596f530b"
      },
      "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": null,
      "id": "f8be4721-91d2-4ae6-8fdb-e91dc6ac1bc5",
      "metadata": {
        "height": 115,
        "id": "f8be4721-91d2-4ae6-8fdb-e91dc6ac1bc5"
      },
      "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": null,
      "id": "e61b095d-9934-41b8-a794-a9dd57e9c733",
      "metadata": {
        "height": 30,
        "id": "e61b095d-9934-41b8-a794-a9dd57e9c733"
      },
      "outputs": [],
      "source": [
        "runnable = prompt | model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "a638efeb-b5ce-4aa4-8377-3e86597a03ab",
      "metadata": {
        "height": 30,
        "id": "a638efeb-b5ce-4aa4-8377-3e86597a03ab",
        "outputId": "3a54b76c-fc3d-4bd6-ef56-4520d0081644"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AIMessage(content='', additional_kwargs={'function_call': {'name': 'weather_search', 'arguments': '{\"airport_code\":\"SFO\"}'}})"
            ]
          },
          "execution_count": 20,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "runnable.invoke({\"input\": \"what is the weather in sf\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3a22faf5-ea24-48d2-b028-03733b548225",
      "metadata": {
        "height": 542,
        "id": "3a22faf5-ea24-48d2-b028-03733b548225"
      },
      "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": null,
      "id": "eb43b030-459f-47e8-a27d-96c2d70cdfef",
      "metadata": {
        "height": 30,
        "id": "eb43b030-459f-47e8-a27d-96c2d70cdfef"
      },
      "outputs": [],
      "source": [
        "model = model.bind(functions=functions)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "3ff03e0d-d6c6-4b47-815e-d7ea5b248567",
      "metadata": {
        "height": 30,
        "id": "3ff03e0d-d6c6-4b47-815e-d7ea5b248567"
      },
      "outputs": [],
      "source": [
        "runnable = prompt | model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "03855fa3-5e2f-4ab2-aba0-c2cd5423239e",
      "metadata": {
        "height": 49,
        "id": "03855fa3-5e2f-4ab2-aba0-c2cd5423239e",
        "outputId": "a8834adc-9d25-42fc-b193-7ab36c92b708"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "AIMessage(content='', additional_kwargs={'function_call': {'name': 'sports_search', 'arguments': '{\"team_name\":\"New England Patriots\"}'}})"
            ]
          },
          "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": {
        "id": "bebc0c55-48c2-4105-90ec-7297553b8e6a"
      },
      "source": [
        "## Fallbacks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "aa0b1ea2-7aef-4449-a553-426cb8c5aa30",
      "metadata": {
        "height": 47,
        "id": "aa0b1ea2-7aef-4449-a553-426cb8c5aa30"
      },
      "outputs": [],
      "source": [
        "from langchain.llms import OpenAI\n",
        "import json"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "bef73d77-9cac-419a-9583-8aefc6c9277b",
      "metadata": {
        "id": "bef73d77-9cac-419a-9583-8aefc6c9277b"
      },
      "source": [
        "**Note**: Due to the deprecation of OpenAI's model `text-davinci-001` on 4 January 2024, you'll be using OpenAI's recommended replacement model `gpt-3.5-turbo-instruct` instead."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "df432efe-9415-42e3-ab57-ecf4d439f369",
      "metadata": {
        "height": 115,
        "id": "df432efe-9415-42e3-ab57-ecf4d439f369"
      },
      "outputs": [],
      "source": [
        "simple_model = OpenAI(\n",
        "    temperature=0,\n",
        "    max_tokens=1000,\n",
        "    model=\"gpt-3.5-turbo-instruct\"\n",
        ")\n",
        "simple_chain = simple_model | json.loads"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "441928c5-8712-45c5-bfdf-6f51634198a7",
      "metadata": {
        "height": 49,
        "id": "441928c5-8712-45c5-bfdf-6f51634198a7"
      },
      "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": null,
      "id": "0739e85f-8497-4471-8ec9-17e958d80771",
      "metadata": {
        "height": 30,
        "id": "0739e85f-8497-4471-8ec9-17e958d80771",
        "outputId": "7f4b2229-8635-45f2-d50e-1ff74b1482ef"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "'\\n\\n{\\n    \"title\": \"Autumn Leaves\",\\n    \"author\": \"Emily Dickinson\",\\n    \"first_line\": \"The leaves are falling, one by one\"\\n}\\n\\n{\\n    \"title\": \"The Ocean\\'s Song\",\\n    \"author\": \"Pablo Neruda\",\\n    \"first_line\": \"I hear the ocean\\'s song, a symphony of waves\"\\n}\\n\\n{\\n    \"title\": \"A Winter\\'s Night\",\\n    \"author\": \"Robert Frost\",\\n    \"first_line\": \"The snow falls softly, covering the ground\"\\n}'"
            ]
          },
          "execution_count": 28,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "simple_model.invoke(challenge)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3a20d15c-dc8a-4b6d-a423-a5f814425219",
      "metadata": {
        "id": "3a20d15c-dc8a-4b6d-a423-a5f814425219"
      },
      "source": [
        "**Note**: The next line is expected to fail."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2a5e8492-0927-4a3a-b939-947826246330",
      "metadata": {
        "height": 30,
        "id": "2a5e8492-0927-4a3a-b939-947826246330",
        "outputId": "f85fb4e2-0778-4c97-c7e4-dd498880e5cb"
      },
      "outputs": [
        {
          "ename": "JSONDecodeError",
          "evalue": "Extra data: line 9 column 1 (char 125)",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mJSONDecodeError\u001b[0m                           Traceback (most recent call last)",
            "Cell \u001b[0;32mIn[29], line 1\u001b[0m\n\u001b[0;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[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/base.py:1113\u001b[0m, in \u001b[0;36mRunnableSequence.invoke\u001b[0;34m(self, input, config)\u001b[0m\n\u001b[1;32m   1111\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1112\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[0;32m-> 1113\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[1;32m   1114\u001b[0m \u001b[43m            \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1115\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[1;32m   1116\u001b[0m \u001b[43m            \u001b[49m\u001b[43mpatch_config\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1117\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[1;32m   1118\u001b[0m \u001b[43m            \u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1119\u001b[0m \u001b[43m        \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1120\u001b[0m \u001b[38;5;66;03m# finish the root run\u001b[39;00m\n\u001b[1;32m   1121\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[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/base.py:2163\u001b[0m, in \u001b[0;36mRunnableLambda.invoke\u001b[0;34m(self, input, config, **kwargs)\u001b[0m\n\u001b[1;32m   2161\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Invoke this runnable synchronously.\"\"\"\u001b[39;00m\n\u001b[1;32m   2162\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[0;32m-> 2163\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[1;32m   2164\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[1;32m   2165\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m   2166\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[1;32m   2167\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2168\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   2169\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mTypeError\u001b[39;00m(\n\u001b[1;32m   2170\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[1;32m   2171\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse `ainvoke` instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m   2172\u001b[0m     )\n",
            "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/base.py:633\u001b[0m, in \u001b[0;36mRunnable._call_with_config\u001b[0;34m(self, func, input, config, run_type, **kwargs)\u001b[0m\n\u001b[1;32m    626\u001b[0m run_manager \u001b[38;5;241m=\u001b[39m callback_manager\u001b[38;5;241m.\u001b[39mon_chain_start(\n\u001b[1;32m    627\u001b[0m     dumpd(\u001b[38;5;28mself\u001b[39m),\n\u001b[1;32m    628\u001b[0m     \u001b[38;5;28minput\u001b[39m,\n\u001b[1;32m    629\u001b[0m     run_type\u001b[38;5;241m=\u001b[39mrun_type,\n\u001b[1;32m    630\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[1;32m    631\u001b[0m )\n\u001b[1;32m    632\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m--> 633\u001b[0m     output \u001b[38;5;241m=\u001b[39m \u001b[43mcall_func_with_variable_args\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m    634\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[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\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[1;32m    635\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    636\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    637\u001b[0m     run_manager\u001b[38;5;241m.\u001b[39mon_chain_error(e)\n",
            "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/config.py:173\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    172\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[0;32m--> 173\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[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/base.py:2096\u001b[0m, in \u001b[0;36mRunnableLambda._invoke\u001b[0;34m(self, input, run_manager, config)\u001b[0m\n\u001b[1;32m   2090\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_invoke\u001b[39m(\n\u001b[1;32m   2091\u001b[0m     \u001b[38;5;28mself\u001b[39m,\n\u001b[1;32m   2092\u001b[0m     \u001b[38;5;28minput\u001b[39m: Input,\n\u001b[1;32m   2093\u001b[0m     run_manager: CallbackManagerForChainRun,\n\u001b[1;32m   2094\u001b[0m     config: RunnableConfig,\n\u001b[1;32m   2095\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Output:\n\u001b[0;32m-> 2096\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[43mrun_manager\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mconfig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2097\u001b[0m     \u001b[38;5;66;03m# If the output is a runnable, invoke it\u001b[39;00m\n\u001b[1;32m   2098\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(output, Runnable):\n",
            "File \u001b[0;32m/usr/local/lib/python3.9/site-packages/langchain/schema/runnable/config.py:173\u001b[0m, in \u001b[0;36mcall_func_with_variable_args\u001b[0;34m(func, input, run_manager, config, **kwargs)\u001b[0m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m accepts_run_manager(func):\n\u001b[1;32m    172\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[0;32m--> 173\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[0;32m/usr/local/lib/python3.9/json/__init__.py:346\u001b[0m, in \u001b[0;36mloads\u001b[0;34m(s, cls, object_hook, parse_float, parse_int, parse_constant, object_pairs_hook, **kw)\u001b[0m\n\u001b[1;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[1;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[1;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[1;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[0;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[1;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[1;32m    348\u001b[0m     \u001b[38;5;28mcls\u001b[39m \u001b[38;5;241m=\u001b[39m JSONDecoder\n",
            "File \u001b[0;32m/usr/local/lib/python3.9/json/decoder.py:340\u001b[0m, in \u001b[0;36mJSONDecoder.decode\u001b[0;34m(self, s, _w)\u001b[0m\n\u001b[1;32m    338\u001b[0m end \u001b[38;5;241m=\u001b[39m _w(s, end)\u001b[38;5;241m.\u001b[39mend()\n\u001b[1;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[0;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[1;32m    341\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m obj\n",
            "\u001b[0;31mJSONDecodeError\u001b[0m: Extra data: line 9 column 1 (char 125)"
          ]
        }
      ],
      "source": [
        "simple_chain.invoke(challenge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6814143b-4a35-4d29-bd32-ba461274bcbf",
      "metadata": {
        "height": 47,
        "id": "6814143b-4a35-4d29-bd32-ba461274bcbf"
      },
      "outputs": [],
      "source": [
        "model = ChatOpenAI(temperature=0)\n",
        "chain = model | StrOutputParser() | json.loads"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f55f04cf-0217-4106-b41f-0e0661d12c27",
      "metadata": {
        "height": 30,
        "id": "f55f04cf-0217-4106-b41f-0e0661d12c27"
      },
      "outputs": [],
      "source": [
        "chain.invoke(challenge)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b5d3f035-b18d-4cba-854d-a43ef8554e48",
      "metadata": {
        "height": 30,
        "id": "b5d3f035-b18d-4cba-854d-a43ef8554e48"
      },
      "outputs": [],
      "source": [
        "final_chain = simple_chain.with_fallbacks([chain])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "9a09fe6a-548c-412d-9468-9efe2b49f7c9",
      "metadata": {
        "height": 30,
        "id": "9a09fe6a-548c-412d-9468-9efe2b49f7c9"
      },
      "outputs": [],
      "source": [
        "final_chain.invoke(challenge)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "3fcfdda0-3db2-4073-a647-f2d62c460349",
      "metadata": {
        "id": "3fcfdda0-3db2-4073-a647-f2d62c460349"
      },
      "source": [
        "## Interface"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "33b3b27f-b5a0-4db5-a1b0-20754437a47e",
      "metadata": {
        "height": 132,
        "id": "33b3b27f-b5a0-4db5-a1b0-20754437a47e"
      },
      "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": null,
      "id": "48c8cabf-ea55-4448-b070-3ec22942c559",
      "metadata": {
        "height": 30,
        "id": "48c8cabf-ea55-4448-b070-3ec22942c559"
      },
      "outputs": [],
      "source": [
        "chain.invoke({\"topic\": \"bears\"})"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "cc58bdb4-a896-46ba-90c4-1b333245229a",
      "metadata": {
        "height": 30,
        "id": "cc58bdb4-a896-46ba-90c4-1b333245229a"
      },
      "outputs": [],
      "source": [
        "chain.batch([{\"topic\": \"bears\"}, {\"topic\": \"frogs\"}])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "8a069d61-0a67-4368-b7c4-367262267bb8",
      "metadata": {
        "height": 47,
        "id": "8a069d61-0a67-4368-b7c4-367262267bb8"
      },
      "outputs": [],
      "source": [
        "for t in chain.stream({\"topic\": \"bears\"}):\n",
        "    print(t)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "2315b43f-c7e1-4f7b-9595-4cabdc019dea",
      "metadata": {
        "height": 47,
        "id": "2315b43f-c7e1-4f7b-9595-4cabdc019dea"
      },
      "outputs": [],
      "source": [
        "response = await chain.ainvoke({\"topic\": \"bears\"})\n",
        "response"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f3082379-f75e-4a74-bee5-c18ddc5ac4dc",
      "metadata": {
        "height": 30,
        "id": "f3082379-f75e-4a74-bee5-c18ddc5ac4dc"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adccca48-787e-4219-af82-f18e6408182b",
      "metadata": {
        "height": 30,
        "id": "adccca48-787e-4219-af82-f18e6408182b"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5c816827-e5b7-480a-826b-0ca715faca3c",
      "metadata": {
        "height": 30,
        "id": "5c816827-e5b7-480a-826b-0ca715faca3c"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15e6d608-c205-4141-bab8-0304dd910978",
      "metadata": {
        "height": 30,
        "id": "15e6d608-c205-4141-bab8-0304dd910978"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "abfc490d-090d-4e1c-b8ee-bf40ec4da4c3",
      "metadata": {
        "height": 30,
        "id": "abfc490d-090d-4e1c-b8ee-bf40ec4da4c3"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5fe48ff0-fd84-4c9e-8069-02117d57f7f0",
      "metadata": {
        "height": 30,
        "id": "5fe48ff0-fd84-4c9e-8069-02117d57f7f0"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "pygments_lexer": "ipython3",
      "version": "3.9.18"
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}