brockmanmatt/qa_from_playgrond.ipynb

## qa_from_playgrond.ipynb
{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "QA_from_playgrond.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMVPCQ06MN/92CSqetjq0bG",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/brockmanmatt/4c32c18e2d5fb4c8cdb441e7c60a8a65/qa_from_playgrond.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "J7wnsgT2kPut",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 86
        },
        "outputId": "38913dc3-5ad9-4d9d-817f-803fd18bc85e"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()\n",
        "print(\"done\")"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7ac01acc-802f-49af-a2fa-dac561ad70bd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7ac01acc-802f-49af-a2fa-dac561ad70bd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving key.json to key.json\n",
            "done\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHPHrUnhpKnI",
        "colab_type": "text"
      },
      "source": [
        "I'll install the API"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zq0ltp2xn4yt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 261
        },
        "outputId": "f32257bb-2de8-4f93-ded5-dc12a8a6fdae"
      },
      "source": [
        "!pip install openai\n",
        "import openai, json, pandas as pd"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting openai\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
            "\r\u001b[K     |██                              | 10kB 16.7MB/s eta 0:00:01\r\u001b[K     |████▏                           | 20kB 2.7MB/s eta 0:00:01\r\u001b[K     |██████▎                         | 30kB 3.5MB/s eta 0:00:01\r\u001b[K     |████████▍                       | 40kB 4.0MB/s eta 0:00:01\r\u001b[K     |██████████▍                     | 51kB 3.2MB/s eta 0:00:01\r\u001b[K     |████████████▌                   | 61kB 3.6MB/s eta 0:00:01\r\u001b[K     |██████████████▋                 | 71kB 3.8MB/s eta 0:00:01\r\u001b[K     |████████████████▊               | 81kB 4.0MB/s eta 0:00:01\r\u001b[K     |██████████████████▊             | 92kB 4.4MB/s eta 0:00:01\r\u001b[K     |████████████████████▉           | 102kB 4.3MB/s eta 0:00:01\r\u001b[K     |███████████████████████         | 112kB 4.3MB/s eta 0:00:01\r\u001b[K     |█████████████████████████       | 122kB 4.3MB/s eta 0:00:01\r\u001b[K     |███████████████████████████     | 133kB 4.3MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 143kB 4.3MB/s eta 0:00:01\r\u001b[K     |███████████████████████████████▎| 153kB 4.3MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 163kB 4.3MB/s \n",
            "\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
            "Building wheels for collected packages: openai\n",
            "  Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170710 sha256=bea0f8f1515b3ea0c03937df48a27b04cbe7734368c2201a16361cb99e22395b\n",
            "  Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
            "Successfully built openai\n",
            "Installing collected packages: openai\n",
            "Successfully installed openai-0.2.4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q2yE0jcnpMEV",
        "colab_type": "text"
      },
      "source": [
        "Loading in key.json that I uploaded; I do this so I don't need to worry about accidently leaking creds if I share the colab (which I'm 99% sure is just a json file that won't expose them)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bwNXXwHen5x9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "openai.api_key = json.load(open(\"key.json\", \"r\"))[\"key\"]"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sXTDJx0An9Bl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def query(prompt, myKwargs = {}, full=False):\n",
        "  \"\"\"\n",
        "  wrapper for the API\n",
        "  \"\"\"\n",
        "  #arguments to send the API\n",
        "  kwargs = {\n",
        "  \"engine\":\"davinci\",\n",
        "  \"temperature\":.25,\n",
        "  \"max_tokens\":150,\n",
        "  \"stop\":\"\\n\\n\",\n",
        "  }\n",
        "\n",
        "  for kwarg in myKwargs:\n",
        "    kwargs[kwarg] = myKwargs[kwarg]\n",
        "\n",
        "  r = openai.Completion.create(prompt=prompt, **kwargs)\n",
        "  if full:\n",
        "    return r\n",
        "  return r[\"choices\"][0][\"text\"].strip()"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EdFXafcJpZ3Q",
        "colab_type": "text"
      },
      "source": [
        "Test to make sure my query works"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4SlyKgjyopPn",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "e0c08654-1a41-4b15-dddc-878cc670e9b0"
      },
      "source": [
        "query(\"q: what is 1+1?\\na:\", myKwargs = {\"stop\":\"\\n\"})"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Rf_JpBL2odp",
        "colab_type": "text"
      },
      "source": [
        "# Straight up copy"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pA2d1Hzdsg-t",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def askQA(question):\n",
        "  prompt = \"\"\"I am a highly intelligent question answering bot. If you ask me a question that is rooted in truth, I will give you the answer. If you ask me a question that is nonsense, trickery, or has no clear answer, I will respond with \"Unknown\".\n",
        "\n",
        "Q: What is human life expectancy in the United States?\n",
        "A: Human life expectancy in the United States is 78 years.\n",
        "\n",
        "Q: Who was president of the United States in 1955?\n",
        "A: Dwight D. Eisenhower was president of the United States in 1955.\n",
        "\n",
        "Q: Which party did he belong to?\n",
        "A: He belonged to the Republican Party.\n",
        "\n",
        "Q: What is the square root of banana?\n",
        "A: Unknown\n",
        "\n",
        "Q: How does a telescope work?\n",
        "A: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n",
        "\n",
        "Q: Where were the 1992 Olympics held?\n",
        "A: The 1992 Olympics were held in Barcelona, Spain.\n",
        "\n",
        "Q: How many squigs are in a bonk?\n",
        "A: Unknown\n",
        "\n",
        "Q:{}\n",
        "A:\"\"\"\n",
        "\n",
        "  return query(prompt.format(question), myKwargs={\"temperature\":0})"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CXmkcSaO0h_W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "1752014b-9cff-4fe0-df20-9c1895e85135"
      },
      "source": [
        "print (askQA(\"What's the capital of spain?\"))"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Madrid is the capital of Spain.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EpbVqndS2n1g",
        "colab_type": "text"
      },
      "source": [
        "# What if want to keep history?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RXoRW3ot0v7S",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "myHistory = []"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2kYNClWv2xMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def askQA(question, history=[]):\n",
        "  \"\"\"\n",
        "  takes history, which is set of dicts [{\"Q\":\"\", \"A\",\"\"}]\n",
        "  \"\"\"\n",
        "  historyString = \"\"\n",
        "  for pair in history:\n",
        "    historyString += \"\\nQ: {}\\n\".format(pair[\"Q\"])\n",
        "    historyString += \"A: {}\\n\".format(pair[\"A\"])\n",
        "  \n",
        "  prompt = \"\"\"I am a highly intelligent question answering bot. If you ask me a question that is rooted in truth, I will give you the answer. If you ask me a question that is nonsense, trickery, or has no clear answer, I will respond with \"Unknown\".\n",
        "\n",
        "Q: What is human life expectancy in the United States?\n",
        "A: Human life expectancy in the United States is 78 years.\n",
        "\n",
        "Q: Who was president of the United States in 1955?\n",
        "A: Dwight D. Eisenhower was president of the United States in 1955.\n",
        "\n",
        "Q: Which party did he belong to?\n",
        "A: He belonged to the Republican Party.\n",
        "\n",
        "Q: What is the square root of banana?\n",
        "A: Unknown\n",
        "\n",
        "Q: How does a telescope work?\n",
        "A: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n",
        "\n",
        "Q: Where were the 1992 Olympics held?\n",
        "A: The 1992 Olympics were held in Barcelona, Spain.\n",
        "\n",
        "Q: How many squigs are in a bonk?\n",
        "A: Unknown\n",
        "{}\n",
        "Q:{}\n",
        "A:\"\"\"\n",
        "\n",
        "  return query(prompt.format(historyString, question), myKwargs={\"temperature\":0})"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZOndkco03OaR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "378faa31-b7a2-4f75-8c83-98fc2b1514a4"
      },
      "source": [
        "myQ = \"what's the capital of spain?\"\n",
        "myA = askQA(myQ)\n",
        "print(myA)\n",
        "myHistory.append({\"Q\":myQ, \"A\":myA})"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Madrid is the capital of Spain.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "850jHVcH33yh",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "88163df0-1050-4c93-bb80-a88fa5c06fd7"
      },
      "source": [
        "myHistory"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'A': 'Madrid is the capital of Spain.', 'Q': \"what's the capital of spain?\"}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5VjUBHFL3awm",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 33
        },
        "outputId": "7ac0297a-9a03-4659-dc2c-0b6190a4160d"
      },
      "source": [
        "myQ = \"what language do they speak there?\"\n",
        "myA = askQA(myQ, history=myHistory)\n",
        "print(myA)\n",
        "myHistory.append({\"Q\":myQ, \"A\":myA})"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Spanish is the language spoken in Spain.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f70IUpwi3giV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 66
        },
        "outputId": "d5331798-6400-479a-d079-17a17e967c2c"
      },
      "source": [
        "myHistory"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[{'A': 'Madrid is the capital of Spain.', 'Q': \"what's the capital of spain?\"},\n",
              " {'A': 'Spanish is the language spoken in Spain.',\n",
              "  'Q': 'what language do they speak there?'}]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CndAyYJP3vjb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}
	{
	"nbformat": 4,
	"nbformat_minor": 0,
	"metadata": {
	"colab": {
	"name": "QA_from_playgrond.ipynb",
	"provenance": [],
	"collapsed_sections": [],
	"authorship_tag": "ABX9TyMVPCQ06MN/92CSqetjq0bG",
	"include_colab_link": true
	},
	"kernelspec": {
	"name": "python3",
	"display_name": "Python 3"
	}
	},
	"cells": [
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "view-in-github",
	"colab_type": "text"
	},
	"source": [
	"<a href=\"https://colab.research.google.com/gist/brockmanmatt/4c32c18e2d5fb4c8cdb441e7c60a8a65/qa_from_playgrond.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "J7wnsgT2kPut",
	"colab_type": "code",
	"colab": {
	"resources": {
	"http://localhost:8080/nbextensions/google.colab/files.js": {
	"data": "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",
	"ok": true,
	"headers": [
	[
	"content-type",
	"application/javascript"
	]
	],
	"status": 200,
	"status_text": ""
	}
	},
	"base_uri": "https://localhost:8080/",
	"height": 86
	},
	"outputId": "38913dc3-5ad9-4d9d-817f-803fd18bc85e"
	},
	"source": [
	"from google.colab import files\n",
	"uploaded = files.upload()\n",
	"print(\"done\")"
	],
	"execution_count": 1,
	"outputs": [
	{
	"output_type": "display_data",
	"data": {
	"text/html": [
	"\n",
	" <input type=\"file\" id=\"files-7ac01acc-802f-49af-a2fa-dac561ad70bd\" name=\"files[]\" multiple disabled\n",
	" style=\"border:none\" />\n",
	" <output id=\"result-7ac01acc-802f-49af-a2fa-dac561ad70bd\">\n",
	" Upload widget is only available when the cell has been executed in the\n",
	" current browser session. Please rerun this cell to enable.\n",
	" </output>\n",
	" <script src=\"/nbextensions/google.colab/files.js\"></script> "
	],
	"text/plain": [
	"<IPython.core.display.HTML object>"
	]
	},
	"metadata": {
	"tags": []
	}
	},
	{
	"output_type": "stream",
	"text": [
	"Saving key.json to key.json\n",
	"done\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "WHPHrUnhpKnI",
	"colab_type": "text"
	},
	"source": [
	"I'll install the API"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "zq0ltp2xn4yt",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 261
	},
	"outputId": "f32257bb-2de8-4f93-ded5-dc12a8a6fdae"
	},
	"source": [
	"!pip install openai\n",
	"import openai, json, pandas as pd"
	],
	"execution_count": 2,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"Collecting openai\n",
	"\u001b[?25l Downloading https://files.pythonhosted.org/packages/a8/65/c7461f4c87984534683f480ea5742777bc39bbf5721123194c2d0347dc1f/openai-0.2.4.tar.gz (157kB)\n",
	"\r\u001b[K \|██ \| 10kB 16.7MB/s eta 0:00:01\r\u001b[K \|████▏ \| 20kB 2.7MB/s eta 0:00:01\r\u001b[K \|██████▎ \| 30kB 3.5MB/s eta 0:00:01\r\u001b[K \|████████▍ \| 40kB 4.0MB/s eta 0:00:01\r\u001b[K \|██████████▍ \| 51kB 3.2MB/s eta 0:00:01\r\u001b[K \|████████████▌ \| 61kB 3.6MB/s eta 0:00:01\r\u001b[K \|██████████████▋ \| 71kB 3.8MB/s eta 0:00:01\r\u001b[K \|████████████████▊ \| 81kB 4.0MB/s eta 0:00:01\r\u001b[K \|██████████████████▊ \| 92kB 4.4MB/s eta 0:00:01\r\u001b[K \|████████████████████▉ \| 102kB 4.3MB/s eta 0:00:01\r\u001b[K \|███████████████████████ \| 112kB 4.3MB/s eta 0:00:01\r\u001b[K \|█████████████████████████ \| 122kB 4.3MB/s eta 0:00:01\r\u001b[K \|███████████████████████████ \| 133kB 4.3MB/s eta 0:00:01\r\u001b[K \|█████████████████████████████▏ \| 143kB 4.3MB/s eta 0:00:01\r\u001b[K \|███████████████████████████████▎\| 153kB 4.3MB/s eta 0:00:01\r\u001b[K \|████████████████████████████████\| 163kB 4.3MB/s \n",
	"\u001b[?25hRequirement already satisfied: requests>=2.20 in /usr/local/lib/python3.6/dist-packages (from openai) (2.23.0)\n",
	"Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (3.0.4)\n",
	"Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2.10)\n",
	"Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (1.24.3)\n",
	"Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests>=2.20->openai) (2020.6.20)\n",
	"Building wheels for collected packages: openai\n",
	" Building wheel for openai (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
	" Created wheel for openai: filename=openai-0.2.4-cp36-none-any.whl size=170710 sha256=bea0f8f1515b3ea0c03937df48a27b04cbe7734368c2201a16361cb99e22395b\n",
	" Stored in directory: /root/.cache/pip/wheels/74/96/c8/c6e170929c276b836613e1b9985343b501fe455e53d85e7d48\n",
	"Successfully built openai\n",
	"Installing collected packages: openai\n",
	"Successfully installed openai-0.2.4\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "Q2yE0jcnpMEV",
	"colab_type": "text"
	},
	"source": [
	"Loading in key.json that I uploaded; I do this so I don't need to worry about accidently leaking creds if I share the colab (which I'm 99% sure is just a json file that won't expose them)"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "bwNXXwHen5x9",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"openai.api_key = json.load(open(\"key.json\", \"r\"))[\"key\"]"
	],
	"execution_count": 3,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "sXTDJx0An9Bl",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"def query(prompt, myKwargs = {}, full=False):\n",
	" \"\"\"\n",
	" wrapper for the API\n",
	" \"\"\"\n",
	" #arguments to send the API\n",
	" kwargs = {\n",
	" \"engine\":\"davinci\",\n",
	" \"temperature\":.25,\n",
	" \"max_tokens\":150,\n",
	" \"stop\":\"\\n\\n\",\n",
	" }\n",
	"\n",
	" for kwarg in myKwargs:\n",
	" kwargs[kwarg] = myKwargs[kwarg]\n",
	"\n",
	" r = openai.Completion.create(prompt=prompt, **kwargs)\n",
	" if full:\n",
	" return r\n",
	" return r[\"choices\"][0][\"text\"].strip()"
	],
	"execution_count": 4,
	"outputs": []
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "EdFXafcJpZ3Q",
	"colab_type": "text"
	},
	"source": [
	"Test to make sure my query works"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "4SlyKgjyopPn",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 35
	},
	"outputId": "e0c08654-1a41-4b15-dddc-878cc670e9b0"
	},
	"source": [
	"query(\"q: what is 1+1?\\na:\", myKwargs = {\"stop\":\"\\n\"})"
	],
	"execution_count": 5,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"application/vnd.google.colaboratory.intrinsic+json": {
	"type": "string"
	},
	"text/plain": [
	"'2'"
	]
	},
	"metadata": {
	"tags": []
	},
	"execution_count": 5
	}
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "8Rf_JpBL2odp",
	"colab_type": "text"
	},
	"source": [
	"# Straight up copy"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "pA2d1Hzdsg-t",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"def askQA(question):\n",
	" prompt = \"\"\"I am a highly intelligent question answering bot. If you ask me a question that is rooted in truth, I will give you the answer. If you ask me a question that is nonsense, trickery, or has no clear answer, I will respond with \"Unknown\".\n",
	"\n",
	"Q: What is human life expectancy in the United States?\n",
	"A: Human life expectancy in the United States is 78 years.\n",
	"\n",
	"Q: Who was president of the United States in 1955?\n",
	"A: Dwight D. Eisenhower was president of the United States in 1955.\n",
	"\n",
	"Q: Which party did he belong to?\n",
	"A: He belonged to the Republican Party.\n",
	"\n",
	"Q: What is the square root of banana?\n",
	"A: Unknown\n",
	"\n",
	"Q: How does a telescope work?\n",
	"A: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n",
	"\n",
	"Q: Where were the 1992 Olympics held?\n",
	"A: The 1992 Olympics were held in Barcelona, Spain.\n",
	"\n",
	"Q: How many squigs are in a bonk?\n",
	"A: Unknown\n",
	"\n",
	"Q:{}\n",
	"A:\"\"\"\n",
	"\n",
	" return query(prompt.format(question), myKwargs={\"temperature\":0})"
	],
	"execution_count": 6,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "CXmkcSaO0h_W",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 33
	},
	"outputId": "1752014b-9cff-4fe0-df20-9c1895e85135"
	},
	"source": [
	"print (askQA(\"What's the capital of spain?\"))"
	],
	"execution_count": 7,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"Madrid is the capital of Spain.\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {
	"id": "EpbVqndS2n1g",
	"colab_type": "text"
	},
	"source": [
	"# What if want to keep history?"
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "RXoRW3ot0v7S",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"myHistory = []"
	],
	"execution_count": 26,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "2kYNClWv2xMF",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	"def askQA(question, history=[]):\n",
	" \"\"\"\n",
	" takes history, which is set of dicts [{\"Q\":\"\", \"A\",\"\"}]\n",
	" \"\"\"\n",
	" historyString = \"\"\n",
	" for pair in history:\n",
	" historyString += \"\\nQ: {}\\n\".format(pair[\"Q\"])\n",
	" historyString += \"A: {}\\n\".format(pair[\"A\"])\n",
	" \n",
	" prompt = \"\"\"I am a highly intelligent question answering bot. If you ask me a question that is rooted in truth, I will give you the answer. If you ask me a question that is nonsense, trickery, or has no clear answer, I will respond with \"Unknown\".\n",
	"\n",
	"Q: What is human life expectancy in the United States?\n",
	"A: Human life expectancy in the United States is 78 years.\n",
	"\n",
	"Q: Who was president of the United States in 1955?\n",
	"A: Dwight D. Eisenhower was president of the United States in 1955.\n",
	"\n",
	"Q: Which party did he belong to?\n",
	"A: He belonged to the Republican Party.\n",
	"\n",
	"Q: What is the square root of banana?\n",
	"A: Unknown\n",
	"\n",
	"Q: How does a telescope work?\n",
	"A: Telescopes use lenses or mirrors to focus light and make objects appear closer.\n",
	"\n",
	"Q: Where were the 1992 Olympics held?\n",
	"A: The 1992 Olympics were held in Barcelona, Spain.\n",
	"\n",
	"Q: How many squigs are in a bonk?\n",
	"A: Unknown\n",
	"{}\n",
	"Q:{}\n",
	"A:\"\"\"\n",
	"\n",
	" return query(prompt.format(historyString, question), myKwargs={\"temperature\":0})"
	],
	"execution_count": 27,
	"outputs": []
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "ZOndkco03OaR",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 33
	},
	"outputId": "378faa31-b7a2-4f75-8c83-98fc2b1514a4"
	},
	"source": [
	"myQ = \"what's the capital of spain?\"\n",
	"myA = askQA(myQ)\n",
	"print(myA)\n",
	"myHistory.append({\"Q\":myQ, \"A\":myA})"
	],
	"execution_count": 28,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"Madrid is the capital of Spain.\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "850jHVcH33yh",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 33
	},
	"outputId": "88163df0-1050-4c93-bb80-a88fa5c06fd7"
	},
	"source": [
	"myHistory"
	],
	"execution_count": 29,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"[{'A': 'Madrid is the capital of Spain.', 'Q': \"what's the capital of spain?\"}]"
	]
	},
	"metadata": {
	"tags": []
	},
	"execution_count": 29
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "5VjUBHFL3awm",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 33
	},
	"outputId": "7ac0297a-9a03-4659-dc2c-0b6190a4160d"
	},
	"source": [
	"myQ = \"what language do they speak there?\"\n",
	"myA = askQA(myQ, history=myHistory)\n",
	"print(myA)\n",
	"myHistory.append({\"Q\":myQ, \"A\":myA})"
	],
	"execution_count": 30,
	"outputs": [
	{
	"output_type": "stream",
	"text": [
	"Spanish is the language spoken in Spain.\n"
	],
	"name": "stdout"
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "f70IUpwi3giV",
	"colab_type": "code",
	"colab": {
	"base_uri": "https://localhost:8080/",
	"height": 66
	},
	"outputId": "d5331798-6400-479a-d079-17a17e967c2c"
	},
	"source": [
	"myHistory"
	],
	"execution_count": 31,
	"outputs": [
	{
	"output_type": "execute_result",
	"data": {
	"text/plain": [
	"[{'A': 'Madrid is the capital of Spain.', 'Q': \"what's the capital of spain?\"},\n",
	" {'A': 'Spanish is the language spoken in Spain.',\n",
	" 'Q': 'what language do they speak there?'}]"
	]
	},
	"metadata": {
	"tags": []
	},
	"execution_count": 31
	}
	]
	},
	{
	"cell_type": "code",
	"metadata": {
	"id": "CndAyYJP3vjb",
	"colab_type": "code",
	"colab": {}
	},
	"source": [
	""
	],
	"execution_count": null,
	"outputs": []
	}
	]
	}