Add JSON schema quickstart

quickstarts/JSON_Schema.ipynb

@@ -0,0 +1,191 @@
+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "source": [
+        "# Gemini API: JSON Schema\n",
+        "\n",
+        "The Gemini API can be used to generate a JSON output if we set the schema that we would like to use."
+      ],
+      "metadata": {
+        "id": "GAsiP4mohC2_"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "qLuL9m7KhvxR",
+        "outputId": "86c668b8-f3a0-40b0-e832-76bb4f82368f"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m679.1/679.1 kB\u001b[0m \u001b[31m7.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for google-generativeai (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "ATIbQM0NHhkj"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai\n",
+        "\n",
+        "import dataclasses\n",
+        "import typing_extensions as typing"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Configure your API key\n",
+        "\n",
+        "To run the following cell, your API key must be stored in a Colab Secret named `GOOGLE_API_KEY`. If you don't already have an API key, or you're not sure how to create a Colab Secret, see [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) for an example."
+      ],
+      "metadata": {
+        "id": "B-axqBTM8Lbd"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "d6lYXRcjthKV"
+      },
+      "outputs": [],
+      "source": [
+        "from google.colab import userdata\n",
+        "GOOGLE_API_KEY=userdata.get('GOOGLE_API_KEY')\n",
+        "\n",
+        "genai.configure(api_key=GOOGLE_API_KEY)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "## Generate JSON\n",
+        "\n",
+        "We can take a Python class, for instance, and use it as our schema for generating JSON."
+      ],
+      "metadata": {
+        "id": "K9nIks0R-tIa"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "class Person(typing.TypedDict):\n",
+        "    family_name: str\n",
+        "    favorite_food: str\n"
+      ],
+      "metadata": {
+        "id": "vP6teXff_H_L"
+      },
+      "execution_count": 5,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "prompt = \"Hello, describe a person, all fields are required.\""
+      ],
+      "metadata": {
+        "id": "p0gxz0NNr8si"
+      },
+      "execution_count": 6,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Using `generate_content`, we pass in the Python class `Person` defined above into the `generation_config`'s `response_schema` field."
+      ],
+      "metadata": {
+        "id": "vBlWzt6M-2oM"
+      }
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "8oe-tL8MDGtx"
+      },
+      "outputs": [],
+      "source": [
+        "model = genai.GenerativeModel(model_name=\"models/gemini-1.5-pro-latest\")\n",
+        "\n",
+        "result = model.generate_content(\n",
+        "    prompt,\n",
+        "    generation_config={\"response_mime_type\": \"application/json\",\n",
+        "                       \"response_schema\": Person\n",
+        "    },\n",
+        "    request_options={\"timeout\": 600},\n",
+        ")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "print(result.text)"
+      ],
+      "metadata": {
+        "id": "slYcVAcqaDQY",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "outputId": "468d90b2-b7a9-4eca-a09e-4d9b46bcc741"
+      },
+      "execution_count": 8,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "{\"family_name\": \"Smith\", \"favorite_food\": \"Pizza\" } \n"
+          ]
+        }
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3 (ipykernel)",
+      "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": "3.11.3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}