gretel-tabular-llm-inference-api-example.ipynb

gretel-tabular-llm-inference-api-example.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/zredlined/2c205049775aaa83ea201d2c1b87bd59/gretel-tabular-llm-inference-api-example.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%%capture\n",
        "!pip install -Uqq gretel_client"
      ],
      "metadata": {
        "id": "-2YO5lg8Ydpj"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "o3pwdd5FYXvx"
      },
      "outputs": [],
      "source": [
        "from gretel_client import Gretel\n",
        "\n",
        "gretel = Gretel(api_key=\"prompt\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "owgebZrfYXvy"
      },
      "outputs": [],
      "source": [
        "navigator = gretel.factories.initialize_inference_api(\"navigator\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3MYr-f5MYXvy"
      },
      "outputs": [],
      "source": [
        "prompt = \"\"\"\\\n",
        "Generate a dataset of characters from The Simpsons.\n",
        "\n",
        "Each character should have the following columns:\n",
        "* first_name: The first name of the character.\n",
        "* last_name: The last name of the character.\n",
        "* favorite_band: The character's all-time favorite band.\n",
        "* favorite_tv_show: The character's favorite TV show other than The Simpsons.\n",
        "* favorite_food: The character's favorite food.\n",
        "* backstory: The character's back story in 3-5 sentences.\n",
        "\"\"\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AYL_0bP2YXvz"
      },
      "outputs": [],
      "source": [
        "df = navigator.generate(prompt=prompt, num_records=10)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "I-uwHc0OYXvz"
      },
      "outputs": [],
      "source": [
        "edit_prompt = \"Please add a column that describes the character's worst nightmare.\"\n",
        "\n",
        "df = navigator.edit(prompt=edit_prompt, seed_data=df)\n",
        "\n",
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jdNdonhlYXvz"
      },
      "outputs": [],
      "source": [
        "for record in navigator.generate(prompt=prompt, num_records=10, stream=True):\n",
        "    print(record)"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "monogretel-dev",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.16"
    },
    "colab": {
      "provenance": [],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}