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+{
+  "cells": [
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "p3A9q4LNh1bL"
+      },
+      "source": [
+        "##### Copyright 2024 Google LLC."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 1,
+      "metadata": {
+        "cellView": "form",
+        "id": "KGxPOhGBh2Xy"
+      },
+      "outputs": [],
+      "source": [
+        "# @title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
+        "# you may not use this file except in compliance with the License.\n",
+        "# You may obtain a copy of the License at\n",
+        "#\n",
+        "# https://www.apache.org/licenses/LICENSE-2.0\n",
+        "#\n",
+        "# Unless required by applicable law or agreed to in writing, software\n",
+        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
+        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
+        "# See the License for the specific language governing permissions and\n",
+        "# limitations under the License."
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "sP8PQnz1QrcF"
+      },
+      "source": [
+        "# Gemini API: Basic information extraction"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "bxGr_x3MRA0z"
+      },
+      "source": [
+        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
+        "  <td>\n",
+        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/google-gemini/cookbook/blob/main/examples/prompting/Basic_Information_Extraction.ipynb\"><img src = \"https://www.tensorflow.org/images/colab_logo_32px.png\"/>Run in Google Colab</a>\n",
+        "  </td>\n",
+        "</table>"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "ysy--KfNRrCq"
+      },
+      "source": [
+        "This example notebook shows how Gemini API's Python SDK can be used to extract information from a block of text and return it in defined structure.\n",
+        "\n",
+        "In this notebook, the LLM is given a recipe and is asked to extract all the ingredients to create a shopping list. According to best practices, complex tasks will be executed better if divided into separate steps, such as:\n",
+        "\n",
+        "1. First, the model will extract all the groceries into a list.\n",
+        "\n",
+        "2. Then, you will prompt it to convert this list into a shopping list.\n",
+        "\n",
+        "You can find more tips for writing prompts [here](https://ai.google.dev/gemini-api/docs/prompting-intro).\n"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 2,
+      "metadata": {
+        "id": "Ne-3gnXqR0hI"
+      },
+      "outputs": [],
+      "source": [
+        "!pip install -U -q google-generativeai"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {
+        "id": "EconMHePQHGw"
+      },
+      "outputs": [],
+      "source": [
+        "import google.generativeai as genai\n",
+        "\n",
+        "from IPython.display import Markdown"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "eomJzCa6lb90"
+      },
+      "source": [
+        "## Configure your API key\n",
+        "\n",
+        "To run the following cell, your API key must be stored it 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."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 4,
+      "metadata": {
+        "id": "v-JZzORUpVR2"
+      },
+      "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",
+      "metadata": {
+        "id": "L-Wt23A_uzFZ"
+      },
+      "source": [
+        "## Example\n",
+        "\n",
+        "First, start by extracting all the groceries. To dod this, set the system instructions when defining the model"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 5,
+      "metadata": {
+        "id": "x-Mf5-Vsw2Ft"
+      },
+      "outputs": [],
+      "source": [
+        "groceries_system_prompt = f\"\"\"\n",
+        "Your task is to extract to a list all the groceries with its quantities based on the provided recipe.\n",
+        "Make sure that groceries are in the order of appearance.\n",
+        "\"\"\"\n",
+        "grocery_extraction_model = genai.GenerativeModel(model_name='gemini-1.5-flash-latest',\n",
+        "                                                 system_instruction=groceries_system_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "4YJRWBbviSeC"
+      },
+      "source": [
+        "Next, the recipe is defined. You will pass the recipe into `generate_content`, and see that the list of groceries was successfully extracted from the input."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 6,
+      "metadata": {
+        "id": "yebFPUvcxDdZ"
+      },
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "- 3 garlic cloves\n",
+            "- knob of fresh ginger\n",
+            "- 3 spring onions\n",
+            "- 2 tbsp clear honey\n",
+            "- 1 orange\n",
+            "- 1 tbsp light soy sauce\n",
+            "- 2 tbsp vegetable oil\n",
+            "- 4 small chicken breast fillets\n",
+            "- 20 button mushrooms\n",
+            "- 20 cherry tomatoes\n",
+            "- 2 large red peppers\n",
+            "- 20 wooden skewers \n",
+            "\n"
+          ]
+        }
+      ],
+      "source": [
+        "recipe = \"\"\"\n",
+        "Step 1:\n",
+        "Grind 3 garlic cloves, knob of fresh ginger, roughly chopped, 3 spring onions to a paste in a food processor.\n",
+        "Add 2 tbsp of clear honey, juice from one orange, 1 tbsp of light soy sauce and 2 tbsp of vegetable oil, then blend again.\n",
+        "Pour the mixture over the cubed chicken from 4 small breast fillets and leave to marnate for at least 1hr.\n",
+        "Toss in the 20 button mushrooms for the last half an hour so the take on some of the flavour, too.\n",
+        "\n",
+        "Step 2:\n",
+        "Thread the chicken, 20 cherry tomatoes, mushrooms and 2 large red peppers onto 20 wooden skewers,\n",
+        "then cook on a griddle pan for 7-8 mins each side or until the chicken is thoroughly cooked and golden brown.\n",
+        "Turn the kebabs frequently and baste with the marinade from time to time until evenly cooked.\n",
+        "Arrange on a platter, and eat with your fingers.\n",
+        "\"\"\"\n",
+        "\n",
+        "grocery_list = grocery_extraction_model.generate_content(recipe)\n",
+        "print(grocery_list.text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "w0IH1dd3jSes"
+      },
+      "source": [
+        "The next step is to further format the shopping list based on the ingredients extracted."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 7,
+      "metadata": {
+        "id": "sU0pld4QQqOe"
+      },
+      "outputs": [],
+      "source": [
+        "shopping_list_system_prompt = \"\"\"\n",
+        "You are given a list of groceries. Complete the following:\n",
+        "- Organize groceries into categories for easier shopping.\n",
+        "- List each item one under another with a checkbox [].\n",
+        "\"\"\"\n",
+        "\n",
+        "shopping_list_model = genai.GenerativeModel(model_name='gemini-1.5-flash-latest',\n",
+        "                                            system_instruction=shopping_list_system_prompt)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "Ea84Nf2rkWX9"
+      },
+      "source": [
+        "Now that you have defined the instructions, you can also decide how you want to format your grocery list. Give the prompt a couple examples, or perform few-shot prompting, so it understands how to format your grocery list."
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 8,
+      "metadata": {
+        "id": "3QSf7m5QxmC-"
+      },
+      "outputs": [
+        {
+          "data": {
+            "text/markdown": [
+              "## PRODUCE\n",
+              "- [ ] 3 garlic cloves\n",
+              "- [ ] knob of fresh ginger\n",
+              "- [ ] 3 spring onions\n",
+              "- [ ] 1 orange\n",
+              "- [ ] 20 button mushrooms\n",
+              "- [ ] 20 cherry tomatoes\n",
+              "- [ ] 2 large red peppers\n",
+              "\n",
+              "## PANTRY\n",
+              "- [ ] 2 tbsp clear honey\n",
+              "- [ ] 1 tbsp light soy sauce\n",
+              "- [ ] 2 tbsp vegetable oil\n",
+              "\n",
+              "## MEAT\n",
+              "- [ ] 4 small chicken breast fillets\n",
+              "\n",
+              "## OTHER\n",
+              "- [ ] 20 wooden skewers \n"
+            ],
+            "text/plain": [
+              "<IPython.core.display.Markdown object>"
+            ]
+          },
+          "execution_count": 8,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
+      "source": [
+        "shopping_list_prompt = f\"\"\"\n",
+        "LIST: 3 tomatoes, 1 turkey, 4 tomatoes\n",
+        "OUTPUT:\n",
+        "## VEGETABLES\n",
+        "- [ ] 7 tomatoes\n",
+        "## MEAT\n",
+        "- [ ] 1 turkey\n",
+        "\n",
+        "LIST: {grocery_list.text}\n",
+        "OUTPUT:\n",
+        "\"\"\"\n",
+        "Markdown(shopping_list_model.generate_content(shopping_list_prompt).text)"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "PhttRO0TD9mN"
+      },
+      "source": [
+        "## Next steps\n",
+        "\n",
+        "Be sure to explore other examples of prompting in the repository. Try creating your own prompts for information extraction or adapt the ones provided in the notebook."
+      ]
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "name": "Basic_Information_Extraction.ipynb",
+      "toc_visible": true
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}