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Add JSON Schema quickstart. #170

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merged 9 commits into from
May 28, 2024
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Add JSON Schema quickstart. #170

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f864891
Add example for adding context information for prompting
shilpakancharla May 21, 2024
a1f4a4b
delete
shilpakancharla May 21, 2024
e57e264
Add JSON schema quickstart
shilpakancharla May 22, 2024
a9fbc9a
Add info about Gemini 1.5 pro
shilpakancharla May 22, 2024
7944d87
Combining JSON mode and schema notebooks
shilpakancharla May 24, 2024
de9396c
Reformatted notebook
shilpakancharla May 24, 2024
95128dd
replace we with you
shilpakancharla May 24, 2024
97909e0
format
MarkDaoust May 28, 2024
84323cf
Use the same example in both halves.
MarkDaoust May 28, 2024
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194 changes: 140 additions & 54 deletions quickstarts/JSON_mode.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -11,7 +11,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {
"cellView": "form",
"id": "tuOe1ymfHZPu"
Expand All @@ -34,7 +34,7 @@
{
"cell_type": "markdown",
"metadata": {
"id": "893sOzyhJDma"
"id": "GAsiP4mohC2_"
},
"source": [
"# Gemini API: JSON Mode Quickstart\n",
Expand All @@ -49,151 +49,165 @@
{
"cell_type": "markdown",
"metadata": {
"id": "h4LQoYRTJIP9"
"id": "lF6sWVRGQ_bi"
},
"source": [
"This notebook demonstrates how to use JSON mode."
"The Gemini API can be used to generate a JSON output if you set the schema that you would like to use.\n",
"\n",
"**Note**: JSON schemas are only supported by Gemini 1.5 Pro right now."
]
},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": null,
"metadata": {
"id": "_PBH7eR9He0I"
"id": "qLuL9m7KhvxR"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m146.8/146.8 kB\u001b[0m \u001b[31m1.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m664.5/664.5 kB\u001b[0m \u001b[31m6.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
"\u001b[?25h"
]
}
],
"outputs": [],
"source": [
"!pip install -qU google-generativeai"
"!pip install -U -q google-generativeai"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "2zwIBNLWJvRf"
"id": "ATIbQM0NHhkj"
},
"outputs": [],
"source": [
"import google.generativeai as genai\n",
"import json"
"\n",
"import json\n",
"import dataclasses\n",
"import typing_extensions as typing"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "F6gHNgcUypVN"
"id": "B-axqBTM8Lbd"
},
"source": [
"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 the [Authentication](https://github.com/google-gemini/cookbook/blob/main/quickstarts/Authentication.ipynb) quickstart for an example."
"## 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."
]
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {
"id": "t0jy9XWjJwv7"
"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",
"metadata": {
"id": "vf42XN1KLcfV"
"id": "hD3qXcOTRD3z"
},
"source": [
"## Activate JSON Mode\n",
"\n",
" Activate JSON mode by specifying `respose_mime_type` in the `generation_config` parameter:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "i5Rod-lXRIhf"
},
"outputs": [],
"source": [
"## Activate JSON mode"
"model = genai.GenerativeModel(\"gemini-1.5-flash-latest\",\n",
" generation_config={\"response_mime_type\": \"application/json\"})"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dC5-79CDMJ3R"
"id": "4071a6143d31"
},
"source": [
"Activate JSON mode by specifying `respose_mime_type` in the `generation_config` parameter."
"For this first example just describe the schema you want back:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "WWq64FXSLXgr"
"id": "K8ezjNb0RJ6Y"
},
"outputs": [],
"source": [
"model = genai.GenerativeModel(\"gemini-1.5-pro-latest\",\n",
" generation_config={\"response_mime_type\": \"application/json\"})"
"prompt = \"\"\"List a few popular cookie recipes using this JSON schema:\n",
"\n",
"Recipe = {'recipe_name': str}\n",
"Return: list[Recipe]\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "Y_djQzyyaCLg"
"id": "ggudoxK8RMlb"
},
"outputs": [],
"source": [
"prompt = \"\"\"List a few popular cookie recipes using this JSON schema:\n",
"{'type': 'object', 'properties': { 'recipe_name': {'type': 'string'}}}\"\"\""
"raw_response = model.generate_content(prompt)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "9TqoNg3VSMYB"
},
"source": [
"Parse the string to JSON:"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "aENeySrWMJN6"
"id": "b99ee66972f5"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[\n",
" {\"recipe_name\": \"Chocolate Chip Cookies\"},\n",
" {\"recipe_name\": \"Peanut Butter Cookies\"},\n",
" {\"recipe_name\": \"Oatmeal Raisin Cookies\"},\n",
" {\"recipe_name\": \"Sugar Cookies\"},\n",
" {\"recipe_name\": \"Shortbread Cookies\"}\n",
"]\n",
"\n",
"\n"
"[{'recipe_name': 'Chocolate Chip Cookies'}, {'recipe_name': 'Oatmeal Raisin Cookies'}, {'recipe_name': 'Snickerdoodles'}, {'recipe_name': 'Sugar Cookies'}, {'recipe_name': 'Peanut Butter Cookies'}]\n"
]
}
],
"source": [
"response = model.generate_content(prompt)\n",
"print(response.text)"
"response = json.loads(raw_response.text)\n",
"print(response)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "pqNsOE1YysLc"
"id": "1092c669169a"
},
"source": [
"Just for fun, parse the string to JSON, and then serialize it."
"For readability searialize and print it:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"execution_count": 7,
"metadata": {
"id": "nb9Z9TdHRzTu"
"id": "WLDPREpmSMu5"
},
"outputs": [
{
Expand All @@ -205,23 +219,95 @@
" \"recipe_name\": \"Chocolate Chip Cookies\"\n",
" },\n",
" {\n",
" \"recipe_name\": \"Peanut Butter Cookies\"\n",
" \"recipe_name\": \"Oatmeal Raisin Cookies\"\n",
" },\n",
" {\n",
" \"recipe_name\": \"Oatmeal Raisin Cookies\"\n",
" \"recipe_name\": \"Snickerdoodles\"\n",
" },\n",
" {\n",
" \"recipe_name\": \"Sugar Cookies\"\n",
" },\n",
" {\n",
" \"recipe_name\": \"Shortbread Cookies\"\n",
" \"recipe_name\": \"Peanut Butter Cookies\"\n",
" }\n",
"]\n"
]
}
],
"source": [
"print(json.dumps(json.loads(response.text), indent=4))"
"print(json.dumps(response, indent=4))"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "K9nIks0R-tIa"
},
"source": [
"## Generate JSON from schema\n",
"\n",
"While `gemini-1.5-flash` models only accept a text description of the JSON you want back, `gemini-1.5-pro` models support \"controlled generation\" (aka \"constrained decoding\"). This allows you to pass a schema object (or a python type equivalent) and the output will strictly follow that schema.\n",
"\n",
"Following the same example as the previous section, here's that recipe type:"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"id": "JiIxKaLl4R0f"
},
"outputs": [],
"source": [
"class Recipe(typing.TypedDict):\n",
" recipe_name: str"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "vBlWzt6M-2oM"
},
"source": [
"For this exaple you want a list of `Recipe` objects, so pass `list[Recipe]` to the `response_schema` field of the `generation_config`."
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"id": "8oe-tL8MDGtx"
},
"outputs": [],
"source": [
"model = genai.GenerativeModel(model_name=\"models/gemini-1.5-pro-latest\")\n",
"\n",
"result = model.generate_content(\n",
" \"List a few popular cookie recipes\",\n",
" generation_config=genai.GenerationConfig(\n",
" response_mime_type=\"application/json\",\n",
" response_schema = list[Recipe]),\n",
" request_options={\"timeout\": 600},\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"id": "slYcVAcqaDQY"
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[{\"recipe_name\": \"Chocolate Chip Cookies\"}, {\"recipe_name\": \"Peanut Butter Cookies\"}, {\"recipe_name\": \"Oatmeal Raisin Cookies\"}, {\"recipe_name\": \"Sugar Cookies\"}, {\"recipe_name\": \"Snickerdoodles\"}] \n"
]
}
],
"source": [
"print(result.text)"
]
}
],
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