Demo_3_Solutions_for_Wrong_Format.ipynb

{
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  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "gpuType": "T4",
      "authorship_tag": "ABX9TyPR8upCdPlsNd9EBfIQ06q1",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dipanjanS/improving-RAG-systems-dhs2024/blob/main/Demo_3_Solutions_for_Wrong_Format.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Solutions for Wrong Format\n",
        "\n",
        "Here we will explore the following strategies\n",
        "\n",
        "- Native LLM Support\n",
        "- Output Parsers\n"
      ],
      "metadata": {
        "id": "b8kkhS-UgJAR"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Install OpenAI, HuggingFace and LangChain dependencies"
      ],
      "metadata": {
        "id": "L1KvMtf54l0d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install langchain\n",
        "!pip install langchain-openai\n",
        "!pip install langchain-community"
      ],
      "metadata": {
        "id": "2evPp14fy258",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d51f351-9919-4245-fd5f-f07551588b2a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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            "  Downloading marshmallow-3.21.3-py3-none-any.whl.metadata (7.1 kB)\n",
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            "Installing collected packages: mypy-extensions, marshmallow, typing-inspect, dataclasses-json, langchain-community\n",
            "Successfully installed dataclasses-json-0.6.7 langchain-community-0.2.10 marshmallow-3.21.3 mypy-extensions-1.0.0 typing-inspect-0.9.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Enter Open AI API Tokens"
      ],
      "metadata": {
        "id": "-E6SF7YdioWZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from getpass import getpass\n",
        "\n",
        "OPENAI_KEY = getpass('Enter Open AI API Key: ')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eeBpx1YEioWg",
        "outputId": "32d8fb54-d19b-4b91-d807-5a9e19781f11"
      },
      "execution_count": null,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Enter Open AI API Key: ··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "os.environ['OPENAI_API_KEY'] = OPENAI_KEY"
      ],
      "metadata": {
        "id": "x1YSuHNF_lbh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Native LLM Output Response Support"
      ],
      "metadata": {
        "id": "yHhc7UKVX7nC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_openai import ChatOpenAI\n",
        "\n",
        "chatgpt = ChatOpenAI(model_name=\"gpt-4o-mini\", temperature=0,\n",
        "                     model_kwargs={\"response_format\": {\"type\": \"json_object\"}})"
      ],
      "metadata": {
        "id": "9qjM8sSzCfg2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "prompt = \"\"\"Who won the Champions league in 2023,\n",
        "            Output should be in JSON and have following fields:\n",
        "            win_team, lose_team, venue, date, score\n",
        "         \"\"\"\n",
        "response = chatgpt.invoke(prompt)"
      ],
      "metadata": {
        "id": "WTHZDxdPCfcP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(response.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-5nr2eBgCfZr",
        "outputId": "d3fe2337-cf54-400f-e8d3-9bb023b0ca14"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{\n",
            "  \"win_team\": \"Manchester City\",\n",
            "  \"lose_team\": \"Inter Milan\",\n",
            "  \"venue\": \"Atatürk Olympic Stadium\",\n",
            "  \"date\": \"June 10, 2023\",\n",
            "  \"score\": \"1-0\"\n",
            "}\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(response.content)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xPEFVAUCDqGV",
        "outputId": "1308059a-cbde-4d34-ed9a-96b75fbc46a6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "str"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Output Parsers"
      ],
      "metadata": {
        "id": "zBGV2SazYBIL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import PromptTemplate\n",
        "from langchain_core.output_parsers import JsonOutputParser\n",
        "from langchain_core.pydantic_v1 import BaseModel, Field\n",
        "\n",
        "\n",
        "# Define your desired data structure - like a python data class.\n",
        "class GameDetails(BaseModel):\n",
        "    win_team: str = Field(description=\"The winning team in the football game\")\n",
        "    lose_team: str = Field(description=\"The losing team in the football game\")\n",
        "    venue: str = Field(description=\"The venue of the football game\")\n",
        "    date: str = Field(description=\"The date of the football game\")\n",
        "    score: str = Field(description=\"The score of the football game\")\n",
        "\n",
        "parser = JsonOutputParser(pydantic_object=GameDetails)"
      ],
      "metadata": {
        "id": "Td0hVmQd3OVw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(parser.get_format_instructions())"
      ],
      "metadata": {
        "id": "xFIk3rqn4RLS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "415ea1f0-aed9-4e60-c074-f66a48ad5e4a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The output should be formatted as a JSON instance that conforms to the JSON schema below.\n",
            "\n",
            "As an example, for the schema {\"properties\": {\"foo\": {\"title\": \"Foo\", \"description\": \"a list of strings\", \"type\": \"array\", \"items\": {\"type\": \"string\"}}}, \"required\": [\"foo\"]}\n",
            "the object {\"foo\": [\"bar\", \"baz\"]} is a well-formatted instance of the schema. The object {\"properties\": {\"foo\": [\"bar\", \"baz\"]}} is not well-formatted.\n",
            "\n",
            "Here is the output schema:\n",
            "```\n",
            "{\"properties\": {\"win_team\": {\"title\": \"Win Team\", \"description\": \"The winning team in the football game\", \"type\": \"string\"}, \"lose_team\": {\"title\": \"Lose Team\", \"description\": \"The losing team in the football game\", \"type\": \"string\"}, \"venue\": {\"title\": \"Venue\", \"description\": \"The venue of the football game\", \"type\": \"string\"}, \"date\": {\"title\": \"Date\", \"description\": \"The date of the football game\", \"type\": \"string\"}, \"score\": {\"title\": \"Score\", \"description\": \"The score of the football game\", \"type\": \"string\"}}, \"required\": [\"win_team\", \"lose_team\", \"venue\", \"date\", \"score\"]}\n",
            "```\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from langchain_core.prompts import PromptTemplate\n",
        "\n",
        "prompt_txt = \"\"\"\n",
        "             Who won the Champions league in 2023\n",
        "             Use the following format when generating the output response\n",
        "\n",
        "             Output format instructions:\n",
        "             {format_instructions}`\n",
        "             \"\"\"\n",
        "\n",
        "prompt = PromptTemplate.from_template(template=prompt_txt)"
      ],
      "metadata": {
        "id": "1HJvSevb4XES"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "llm_chain = (prompt\n",
        "              |\n",
        "            chatgpt\n",
        "              |\n",
        "            parser)\n",
        "\n",
        "response = llm_chain.invoke({\"format_instructions\": parser.get_format_instructions()})"
      ],
      "metadata": {
        "id": "ObjYJrXh8Hj3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "response"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWdKHqCIF5qO",
        "outputId": "12b4ea4c-01d4-4cc5-c2ec-fa2bb1a1df90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'win_team': 'Manchester City',\n",
              " 'lose_team': 'Inter Milan',\n",
              " 'venue': 'Atatürk Olympic Stadium',\n",
              " 'date': '2023-06-10',\n",
              " 'score': '1-0'}"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "type(response)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2x98vgfF62K",
        "outputId": "5681c383-5c6d-4d0e-887e-3de644bf8a77"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    }
  ]
}