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Get started with Gemini using the REST API

If you want to quickly try out the Gemini API, you can use curl commands to call the methods in the REST API. The examples in this tutorial show calls for each API method.

The Colab uses Python code to set an environment variable and to display an image, but you don't need Colab to work with the REST API. You should be able to run all of the curl examples outside of Colab, without modification, as long as you have API_KEY set as described in the next section.

For each curl command, you must specify the applicable model name and your API key.

Set up your API key

To use the Gemini API, you'll need an API key. If you don't already have one, create a key in Google AI Studio.

Get an API key

In Colab, add the key to the secrets manager under the "🔑" in the left panel. Give it the name GOOGLE_API_KEY. You can then add it as an environment variable to pass the key in your curl call.

In a terminal, you can just run GOOGLE_API_KEY="Your API Key".

import os
from google.colab import userdata

os.environ['GOOGLE_API_KEY'] = userdata.get('GOOGLE_API_KEY')

Gemini and `Content` based APIs

Text-only input

Use the generateContent method to generate a response from the model given an input message. If the input contains only text, use the gemini-pro model.

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{
          "text": "Write a story about a magic backpack."}]}]}' 2> /dev/null

{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "In the quaint town of Willow Creek, nestled amidst rolling hills and whispering willows, there lived an ordinary boy named Ethan. Ethan's life took an extraordinary turn the day he stumbled upon an enigmatic backpack hidden in the depths of his attic.\n\nCuriosity ignited within Ethan as he lifted the worn leather straps and unzipped its mysterious contents. Inside lay a shimmering array of vibrant objects and peculiar trinkets. There was a glowing orb that pulsated with an ethereal glow, a feather that seemed to have a life of its own, and a small, enigmatic key.\n\nAs Ethan explored each item, he realized they possessed astonishing abilities. The orb illuminated his path, casting a warm glow in the darkest of nights. The feather granted him the power of flight, allowing him to soar through the skies with newfound freedom. And the key opened a portal to a hidden world, a realm of endless wonder.\n\nArmed with his magical backpack, Ethan embarked on countless adventures. He flew over the towering mountains of Willow Creek, exploring their hidden secrets. He navigated the treacherous depths of the Enchanted Forest, where he encountered mythical creatures and ancient spirits. And he ventured into distant, unknown lands, uncovering lost civilizations and forgotten treasures.\n\nWith each adventure, Ethan's knowledge and abilities grew. He learned to harness the power of his backpack wisely, using its magic to help others and protect the world from evil forces. The backpack became an extension of himself, a symbol of hope and wonder in the face of adversity.\n\nAs the years went by, Ethan's reputation as the boy with the magic backpack spread far and wide. People from all walks of life came to him, seeking his guidance and protection. And Ethan never hesitated to lend a helping hand, using his extraordinary abilities to make the world a better place.\n\nIn the end, the magic backpack became more than just a collection of objects. It was a representation of Ethan's unwavering spirit, his boundless imagination, and his unwavering belief in the power of dreams. And as long as Ethan carried it with him, the magic of Willow Creek would live on, illuminating the darkest corners of the world with hope, wonder, and the limitless possibilities that resided within the heart of a child."
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0,
      "safetyRatings": [
        {
          "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_HATE_SPEECH",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_HARASSMENT",
          "probability": "NEGLIGIBLE"
        },
        {
          "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
          "probability": "NEGLIGIBLE"
        }
      ]
    }
  ],
  "promptFeedback": {
    "safetyRatings": [
      {
        "category": "HARM_CATEGORY_SEXUALLY_EXPLICIT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HATE_SPEECH",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_HARASSMENT",
        "probability": "NEGLIGIBLE"
      },
      {
        "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
        "probability": "NEGLIGIBLE"
      }
    ]
  }
}

Text-and-image input

If the input contains both text and image, use the gemini-pro-vision model. The following snippets help you build a request and send it to the REST API.

curl -o image.jpg https://storage.googleapis.com/generativeai-downloads/images/scones.jpg

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed
100  385k  100  385k    0     0  2053k      0 --:--:-- --:--:-- --:--:-- 2050k

import PIL.Image

img = PIL.Image.open("image.jpg")
img.resize((512, int(img.height*512/img.width)))

png

echo '{
  "contents":[
    {
      "parts":[
        {"text": "What is this picture?"},
        {
          "inline_data": {
            "mime_type":"image/jpeg",
            "data": "'$(base64 -w0 image.jpg)'"
          }
        }
      ]
    }
  ]
}' > request.json

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro-vision:generateContent?key=${GOOGLE_API_KEY} \
        -H 'Content-Type: application/json' \
        -d @request.json 2> /dev/null | grep "text"

"text": " The picture shows a table with a white tablecloth. On the table are two cups of coffee, a bowl of blueberries, and a plate of scones. There are also some flowers on the table."

Multi-turn conversations (chat)

Using Gemini, you can build freeform conversations across multiple turns.

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [
        {"role":"user",
         "parts":[{
           "text": "Write the first line of a story about a magic backpack."}]},
        {"role": "model",
         "parts":[{
           "text": "In the bustling city of Meadow brook, lived a young girl named Sophie. She was a bright and curious soul with an imaginative mind."}]},
        {"role": "user",
         "parts":[{
           "text": "Can you set it in a quiet village in 1600s France?"}]},
      ]
    }' 2> /dev/null | grep "text"

"text": "In the quaint village of Fleur-de-Lys, nestled amidst the rolling hills of 17th century France, lived a young maiden named Antoinette. She possessed a heart brimming with curiosity and a spirit as vibrant as the wildflowers that bloomed in the meadows.\n\nOne sunny morn, as Antoinette strolled through the cobblestone streets, her gaze fell upon a peculiar sight—a weathered leather backpack resting atop a mossy stone bench. Intrigued, she cautiously approached the bag, her fingers tracing the intricate carvings etched into its surface. As her fingertips grazed the worn leather, a surge of warmth coursed through her body, and the backpack began to emit a soft, ethereal glow."

Configuration

Every prompt you send to the model includes parameter values that control how the model generates a response. The model can generate different results for different parameter values. Learn more about model parameters.

Also, you can use safety settings to adjust the likelihood of getting responses that may be considered harmful. By default, safety settings block content with medium and/or high probability of being unsafe content across all dimensions. Learn more about safety settings.

The following example specifies values for all the parameters of the generateContent method.

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:generateContent?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
        "contents": [{
            "parts":[
                {"text": "Write a story about a magic backpack."}
            ]
        }],
        "safetySettings": [
            {
                "category": "HARM_CATEGORY_DANGEROUS_CONTENT",
                "threshold": "BLOCK_ONLY_HIGH"
            }
        ],
        "generationConfig": {
            "stopSequences": [
                "Title"
            ],
            "temperature": 1.0,
            "maxOutputTokens": 800,
            "topP": 0.8,
            "topK": 10
        }
    }'  2> /dev/null | grep "text"

"text": "Once upon a time, in a small town nestled at the foot of a majestic mountain range, lived a young girl named Lily. Lily was a bright and curious child who loved to explore the world around her. One day, while playing in the forest near her home, she stumbled upon a hidden cave. Intrigued, she stepped inside, and to her amazement, she discovered a dusty old backpack lying in a corner.\n\nCuriosity piqued, Lily reached out and picked up the backpack. As soon as her fingers brushed against the worn leather, she felt a strange tingling sensation coursing through her body. Suddenly, the backpack began to glow, emitting a soft, ethereal light that filled the cave.\n\nWith wide-eyed wonder, Lily opened the backpack to find it filled with an assortment of magical objects. There was a compass that always pointed to the nearest adventure, a magnifying glass that could reveal hidden secrets, a telescope that allowed her to see distant lands, and a book that contained the knowledge of the universe.\n\nOverjoyed with her discovery, Lily took the magic backpack home and began using its contents to explore the world in ways she had never imagined. She followed the compass to discover hidden treasures, used the magnifying glass to uncover the secrets of nature, gazed through the telescope to witness the wonders of the cosmos, and delved into the book to learn about the mysteries of the universe.\n\nAs Lily's adventures continued, she realized that the magic backpack was more than just a collection of enchanted items. It was a symbol of her own limitless potential and the power of her imagination. It taught her that with curiosity, courage, and a touch of magic, anything was possible.\n\nNews of Lily's magical backpack spread throughout the town, and soon, children from all around came to her, eager to learn about its wonders. Lily welcomed them with open arms, sharing her stories and inspiring them to embark on their own adventures.\n\nAnd so, the magic backpack became a beacon of hope and wonder, reminding everyone that the world is full of hidden treasures waiting to be discovered, if only one has the courage to step into the unknown."

Stream Generate Content

The generateContent method returns a response after completing the entire generation process. You can achieve faster interactions by not waiting for the entire result, and instead use streamGenerateContent to return partial results.

!curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:streamGenerateContent?alt=sse&key=${GOOGLE_API_KEY}" \
        -H 'Content-Type: application/json' \
        --no-buffer \
        -d '{ "contents":[{"parts":[{"text": "Write long a story about a magic backpack."}]}]}' \
        2> /dev/null

data: {"candidates": [{"content": {"parts": [{"text": "In the quaint little town of Willow Creek, nestled among rolling hills and whispering willows"}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}],"promptFeedback": {"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]} }

data: {"candidates": [{"content": {"parts": [{"text": ", there existed an extraordinary backpack that possessed an astonishing secret. Its unassuming canvas exterior and worn leather straps held a hidden realm brimming with wonder and endless possibilities."}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"parts": [{"text": "\n\nYoung Oliver, a curious and imaginative boy, stumbled upon this magical backpack in the dusty attic of his grandmother's house. Intrigued by its enigmatic aura, he unzipped it cautiously, revealing a seemingly ordinary interior. But as his fingers brushed against the lining, an ethereal glow emanated from within.\n\n"}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"parts": [{"text": "With a gasp of surprise, Oliver watched as the backpack transformed before his very eyes. Its fabric shimmered and flowed like liquid silver, morphing into a portal that connected him to a hidden dimension. Step by step, he ventured into this enchanted realm, his heart pounding with a mixture of trepidation and exhilaration.\n\nThe backpack's interior was a vast and wondrous labyrinth filled with towering bookshelves, bubbling potions, and ethereal artifacts. Each turn offered a new discovery: a self-playing piano, a talking mirror that whispered ancient wisdom, and a compass that pointed to the furthest reaches of the imagination.\n\nOliver soon realized"}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"parts": [{"text": " that this backpack was no mere container but a sentient being, capable of aiding him in his quests and expanding his horizons. It granted him the gift of tongues, allowing him to speak with animals and creatures from distant lands. It gifted him a quill that wrote stories that danced off the page, bringing his wildest dreams to life.\n\nTogether, Oliver and the backpack embarked on extraordinary adventures. They soared through the skies on the back of a majestic griffon, traversed treacherous terrains with the aid of a shape-shifting fox, and solved mysteries that had long baffled the wisest minds in Willow Creek.\n\nAs the days turned into weeks, Oliver's imagination flourished beyond measure. He painted vibrant landscapes with words, composed symphonies that echoed through the hidden realm, and invented gadgets that defied the laws of physics. The backpack became an extension of his boundless creativity, nurturing his wonder and fueling his aspirations.\n\nNews of Oliver's extraordinary backpack spread throughout the town and beyond. People flocked from far and wide to witness its marvels. Scholars sought its wisdom, artists sought its inspiration, and children dreamed of experiencing its boundless adventures.\n\nHowever, not all who approached the backpack with pure intentions. One fateful day, a greedy sorcerer named Maldred attempted to seize its power for himself. But"}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

data: {"candidates": [{"content": {"parts": [{"text": " the backpack, sensing his malevolent nature, resisted his grasp and summoned a legion of fantastical creatures to its defense.\n\nIn a fierce battle that shook the very fabric of the hidden realm, Oliver and the backpack allied with brave heroes and wise wizards to defeat Maldred and his wicked forces. The town of Willow Creek was forever grateful, and the backpack became a symbol of hope and imagination for all who knew of its existence.\n\nAs the years passed, Oliver grew into a wise and compassionate leader, using the magic backpack to spread joy, inspire creativity, and unlock the potential of those around him. The hidden realm within its depths became a sanctuary for dreamers, inventors, and anyone who dared to embrace the wonders of the unknown.\n\nAnd so, the tale of the magic backpack was passed down through generations, a timeless testament to the power of imagination and the boundless possibilities that lie when wonder and curiosity ignite the human spirit."}],"role": "model"},"finishReason": "STOP","index": 0,"safetyRatings": [{"category": "HARM_CATEGORY_SEXUALLY_EXPLICIT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HATE_SPEECH","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_HARASSMENT","probability": "NEGLIGIBLE"},{"category": "HARM_CATEGORY_DANGEROUS_CONTENT","probability": "NEGLIGIBLE"}]}]}

Count tokens

When using long prompts, it might be useful to count tokens before sending any content to the model.

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro:countTokens?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "contents": [{
        "parts":[{
          "text": "Write a story about a magic backpack."}]}]}' > response.json

% Total    % Received % Xferd  Average Speed   Time    Time     Time  Current
                                 Dload  Upload   Total   Spent    Left  Speed

  0     0    0     0    0     0      0      0 --:--:-- --:--:-- --:--:--     0
100   127    0    23  100   104    105    477 --:--:-- --:--:-- --:--:--   585

cat response.json

{
  "totalTokens": 8
}

Embedding

Embedding is a technique used to represent information as a list of floating point numbers in an array. With Gemini, you can represent text (words, sentences, and blocks of text) in a vectorized form, making it easier to compare and contrast embeddings. For example, two texts that share a similar subject matter or sentiment should have similar embeddings, which can be identified through mathematical comparison techniques such as cosine similarity.

Use the embedding-001 model with either embedContents or batchEmbedContents:

curl https://generativelanguage.googleapis.com/v1beta/models/embedding-001:embedContent?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
        "model": "models/embedding-001",
        "content": {
        "parts":[{
          "text": "Write a story about a magic backpack."}]} }' 2> /dev/null | head

{
  "embedding": {
    "values": [
      0.008624583,
      -0.030451821,
      -0.042496547,
      -0.029230341,
      0.05486475,
      0.006694871,
      0.004025645,

curl https://generativelanguage.googleapis.com/v1beta/models/embedding-001:batchEmbedContents?key=$GOOGLE_API_KEY \
    -H 'Content-Type: application/json' \
    -X POST \
    -d '{
      "requests": [{
        "model": "models/embedding-001",
        "content": {
        "parts":[{
          "text": "Write a story about a magic backpack."}]} }]}' 2> /dev/null | head

{
  "embeddings": [
    {
      "values": [
        0.008624583,
        -0.030451821,
        -0.042496547,
        -0.029230341,
        0.05486475,
        0.006694871,

Model info

Get model

If you GET a model's URL, the API uses the get method to return information about that model such as version, display name, input token limit, etc.

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-pro?key=$GOOGLE_API_KEY

{
  "name": "models/gemini-pro",
  "version": "001",
  "displayName": "Gemini Pro",
  "description": "The best model for scaling across a wide range of tasks",
  "inputTokenLimit": 30720,
  "outputTokenLimit": 2048,
  "supportedGenerationMethods": [
    "generateContent",
    "countTokens"
  ],
  "temperature": 0.9,
  "topP": 1,
  "topK": 1
}

List models

If you GET the models directory, it uses the list method to list all of the models available through the API, including both the Gemini and PaLM family models.

curl https://generativelanguage.googleapis.com/v1beta/models?key=$GOOGLE_API_KEY

{
  "models": [
    {
      "name": "models/chat-bison-001",
      "version": "001",
      "displayName": "Chat Bison",
      "description": "Chat-optimized generative language model.",
      "inputTokenLimit": 4096,
      "outputTokenLimit": 1024,
      "supportedGenerationMethods": [
        "generateMessage",
        "countMessageTokens"
      ],
      "temperature": 0.25,
      "topP": 0.95,
      "topK": 40
    },
    {
      "name": "models/text-bison-001",
      "version": "001",
      "displayName": "Text Bison",
      "description": "Model targeted for text generation.",
      "inputTokenLimit": 8196,
      "outputTokenLimit": 1024,
      "supportedGenerationMethods": [
        "generateText",
        "countTextTokens",
        "createTunedTextModel"
      ],
      "temperature": 0.7,
      "topP": 0.95,
      "topK": 40
    },
    {
      "name": "models/embedding-gecko-001",
      "version": "001",
      "displayName": "Embedding Gecko",
      "description": "Obtain a distributed representation of a text.",
      "inputTokenLimit": 1024,
      "outputTokenLimit": 1,
      "supportedGenerationMethods": [
        "embedText",
        "countTextTokens"
      ]
    },
    {
      "name": "models/embedding-gecko-002",
      "version": "002",
      "displayName": "Embedding Gecko 002",
      "description": "Obtain a distributed representation of a text.",
      "inputTokenLimit": 2048,
      "outputTokenLimit": 1,
      "supportedGenerationMethods": [
        "embedText",
        "countTextTokens"
      ]
    },
    {
      "name": "models/gemini-pro",
      "version": "001",
      "displayName": "Gemini Pro",
      "description": "The best model for scaling across a wide range of tasks",
      "inputTokenLimit": 30720,
      "outputTokenLimit": 2048,
      "supportedGenerationMethods": [
        "generateContent",
        "countTokens"
      ],
      "temperature": 0.9,
      "topP": 1,
      "topK": 1
    },
    {
      "name": "models/gemini-pro-vision",
      "version": "001",
      "displayName": "Gemini Pro Vision",
      "description": "The best image understanding model to handle a broad range of applications",
      "inputTokenLimit": 12288,
      "outputTokenLimit": 4096,
      "supportedGenerationMethods": [
        "generateContent",
        "countTokens"
      ],
      "temperature": 0.4,
      "topP": 1,
      "topK": 32
    },
    {
      "name": "models/gemini-ultra",
      "version": "001",
      "displayName": "Gemini Ultra",
      "description": "The most capable model for highly complex tasks",
      "inputTokenLimit": 30720,
      "outputTokenLimit": 2048,
      "supportedGenerationMethods": [
        "generateContent",
        "countTokens"
      ],
      "temperature": 0.9,
      "topP": 1,
      "topK": 32
    },
    {
      "name": "models/embedding-001",
      "version": "001",
      "displayName": "Embedding 001",
      "description": "Obtain a distributed representation of a text.",
      "inputTokenLimit": 2048,
      "outputTokenLimit": 1,
      "supportedGenerationMethods": [
        "embedContent",
        "countTextTokens"
      ]
    },
    {
      "name": "models/aqa",
      "version": "001",
      "displayName": "Model that performs Attributed Question Answering.",
      "description": "Model trained to return answers to questions that are grounded in provided sources, along with estimating answerable probability.",
      "inputTokenLimit": 7168,
      "outputTokenLimit": 1024,
      "supportedGenerationMethods": [
        "generateAnswer"
      ],
      "temperature": 0.2,
      "topP": 1,
      "topK": 40
    }
  ]
}