Text generation

The Gemini API can generate text output from various inputs, including text, images, video, and audio, leveraging Gemini models.

Here's a basic example that takes a single text input:

PythonJavaScriptGoRESTApps Script 
from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=["How does AI work?"]
)
print(response.text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: "How does AI work?",
  });
  console.log(response.text);
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  result, _ := client.Models.GenerateContent(
      ctx,
      "gemini-2.0-flash",
      genai.Text("Explain how AI works in a few words"),
      nil,
  )

  fmt.Println(result.Text())
}

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "How does AI work?"
          }
        ]
      }
    ]
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const payload = {
    contents: [
      {
        parts: [
          { text: 'How AI does work?' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

System instructions and configuration

You can guide the behavior of Gemini models with system instructions. To do so, pass a GenerateContentConfig object.

PythonJavaScriptGoRESTApps Script 
from google import genai
from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    config=types.GenerateContentConfig(
        system_instruction="You are a cat. Your name is Neko."),
    contents="Hello there"
)

print(response.text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: "Hello there",
    config: {
      systemInstruction: "You are a cat. Your name is Neko.",
    },
  });
  console.log(response.text);
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  config := &genai.GenerateContentConfig{
      SystemInstruction: genai.NewContentFromText("You are a cat. Your name is Neko.", genai.RoleUser),
  }

  result, _ := client.Models.GenerateContent(
      ctx,
      "gemini-2.0-flash",
      genai.Text("Hello there"),
      config,
  )

  fmt.Println(result.Text())
}

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
    "system_instruction": {
      "parts": [
        {
          "text": "You are a cat. Your name is Neko."
        }
      ]
    },
    "contents": [
      {
        "parts": [
          {
            "text": "Hello there"
          }
        ]
      }
    ]
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const systemInstruction = {
    parts: [{
      text: 'You are a cat. Your name is Neko.'
    }]
  };

  const payload = {
    systemInstruction,
    contents: [
      {
        parts: [
          { text: 'Hello there' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

The GenerateContentConfig object also lets you override default generation parameters, such as temperature.

PythonJavaScriptGoRESTApps Script 
from google import genai
from google.genai import types

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=["Explain how AI works"],
    config=types.GenerateContentConfig(
        max_output_tokens=500,
        temperature=0.1
    )
)
print(response.text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: "Explain how AI works",
    config: {
      maxOutputTokens: 500,
      temperature: 0.1,
    },
  });
  console.log(response.text);
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
    APIKey:  os.Getenv("GEMINI_API_KEY"),
    Backend: genai.BackendGeminiAPI,
  })

  temp := float32(0.9)
  topP := float32(0.5)
  topK := float32(20.0)
  maxOutputTokens := int32(100)

  config := &genai.GenerateContentConfig{
    Temperature:       &temp,
    TopP:              &topP,
    TopK:              &topK,
    MaxOutputTokens:   maxOutputTokens,
    ResponseMIMEType:  "application/json",
  }

  result, _ := client.Models.GenerateContent(
    ctx,
    "gemini-2.0-flash",
    genai.Text("What is the average size of a swallow?"),
    config,
  )

  fmt.Println(result.Text())
}

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "Explain how AI works"
          }
        ]
      }
    ],
    "generationConfig": {
      "stopSequences": [
        "Title"
      ],
      "temperature": 1.0,
      "maxOutputTokens": 800,
      "topP": 0.8,
      "topK": 10
    }
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const generationConfig = {
    temperature: 1,
    topP: 0.95,
    topK: 40,
    maxOutputTokens: 8192,
    responseMimeType: 'text/plain',
  };

  const payload = {
    generationConfig,
    contents: [
      {
        parts: [
          { text: 'Explain how AI works in a few words' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

Refer to the GenerateContentConfig in our API reference for a complete list of configurable parameters and their descriptions.

Multimodal inputs

The Gemini API supports multimodal inputs, allowing you to combine text with media files. The following example demonstrates providing an image:

PythonJavaScriptGoRESTApps Script 
from PIL import Image
from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")

image = Image.open("/path/to/organ.png")
response = client.models.generate_content(
    model="gemini-2.0-flash",
    contents=[image, "Tell me about this instrument"]
)
print(response.text)

import {
  GoogleGenAI,
  createUserContent,
  createPartFromUri,
} from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const image = await ai.files.upload({
    file: "/path/to/organ.png",
  });
  const response = await ai.models.generateContent({
    model: "gemini-2.0-flash",
    contents: [
      createUserContent([
        "Tell me about this instrument",
        createPartFromUri(image.uri, image.mimeType),
      ]),
    ],
  });
  console.log(response.text);
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  imagePath := "/path/to/organ.jpg"
  imgData, _ := os.ReadFile(imagePath)

  parts := []*genai.Part{
      genai.NewPartFromText("Tell me about this instrument"),
      &genai.Part{
          InlineData: &genai.Blob{
              MIMEType: "image/jpeg",
              Data:     imgData,
          },
      },
  }

  contents := []*genai.Content{
      genai.NewContentFromParts(parts, genai.RoleUser),
  }

  result, _ := client.Models.GenerateContent(
      ctx,
      "gemini-2.0-flash",
      contents,
      nil,
  )

  fmt.Println(result.Text())
}

# Use a temporary file to hold the base64 encoded image data
TEMP_B64=$(mktemp)
trap 'rm -f "$TEMP_B64"' EXIT
base64 $B64FLAGS $IMG_PATH > "$TEMP_B64"

# Use a temporary file to hold the JSON payload
TEMP_JSON=$(mktemp)
trap 'rm -f "$TEMP_JSON"' EXIT

cat > "$TEMP_JSON" << EOF
{
  "contents": [
    {
      "parts": [
        {
          "text": "Tell me about this instrument"
        },
        {
          "inline_data": {
            "mime_type": "image/jpeg",
            "data": "$(cat "$TEMP_B64")"
          }
        }
      ]
    }
  ]
}
EOF

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY" \
  -H 'Content-Type: application/json' \
  -X POST \
  -d "@$TEMP_JSON"

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const imageUrl = 'http://image/url';
  const image = getImageData(imageUrl);
  const payload = {
    contents: [
      {
        parts: [
          { image },
          { text: 'Tell me about this instrument' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

function getImageData(url) {
  const blob = UrlFetchApp.fetch(url).getBlob();

  return {
    mimeType: blob.getContentType(),
    data: Utilities.base64Encode(blob.getBytes())
  };
}

For alternative methods of providing images and more advanced image processing, see our image understanding guide. The API also supports document, video, and audio inputs and understanding.

Streaming responses

By default, the model returns a response only after the entire generation process is complete.

For more fluid interactions, use streaming to receive GenerateContentResponse instances incrementally as they're generated.

PythonJavaScriptGoRESTApps Script 
from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")

response = client.models.generate_content_stream(
    model="gemini-2.0-flash",
    contents=["Explain how AI works"]
)
for chunk in response:
    print(chunk.text, end="")

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const response = await ai.models.generateContentStream({
    model: "gemini-2.0-flash",
    contents: "Explain how AI works",
  });

  for await (const chunk of response) {
    console.log(chunk.text);
  }
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  stream := client.Models.GenerateContentStream(
      ctx,
      "gemini-2.0-flash",
      genai.Text("Write a story about a magic backpack."),
      nil,
  )

  for chunk, _ := range stream {
      part := chunk.Candidates[0].Content.Parts[0]
      fmt.Print(part.Text)
  }
}

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=${GEMINI_API_KEY}" \
  -H 'Content-Type: application/json' \
  --no-buffer \
  -d '{
    "contents": [
      {
        "parts": [
          {
            "text": "Explain how AI works"
          }
        ]
      }
    ]
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const payload = {
    contents: [
      {
        parts: [
          { text: 'Explain how AI works' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

Multi-turn conversations (Chat)

Our SDKs provide functionality to collect multiple rounds of prompts and responses into a chat, giving you an easy way to keep track of the conversation history.

PythonJavaScriptGoRESTApps Script 
from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")
chat = client.chats.create(model="gemini-2.0-flash")

response = chat.send_message("I have 2 dogs in my house.")
print(response.text)

response = chat.send_message("How many paws are in my house?")
print(response.text)

for message in chat.get_history():
    print(f'role - {message.role}',end=": ")
    print(message.parts[0].text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const chat = ai.chats.create({
    model: "gemini-2.0-flash",
    history: [
      {
        role: "user",
        parts: [{ text: "Hello" }],
      },
      {
        role: "model",
        parts: [{ text: "Great to meet you. What would you like to know?" }],
      },
    ],
  });

  const response1 = await chat.sendMessage({
    message: "I have 2 dogs in my house.",
  });
  console.log("Chat response 1:", response1.text);

  const response2 = await chat.sendMessage({
    message: "How many paws are in my house?",
  });
  console.log("Chat response 2:", response2.text);
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  history := []*genai.Content{
      genai.NewContentFromText("Hi nice to meet you! I have 2 dogs in my house.", genai.RoleUser),
      genai.NewContentFromText("Great to meet you. What would you like to know?", genai.RoleModel),
  }

  chat, _ := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history)
  res, _ := chat.SendMessage(ctx, genai.Part{Text: "How many paws are in my house?"})

  if len(res.Candidates) > 0 {
      fmt.Println(res.Candidates[0].Content.Parts[0].Text)
  }
}

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=$GEMINI_API_KEY \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "text": "Hello"
          }
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Great to meet you. What would you like to know?"
          }
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "I have two dogs in my house. How many paws are in my house?"
          }
        ]
      }
    ]
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const payload = {
    contents: [
      {
        role: 'user',
        parts: [
          { text: 'Hello' },
        ],
      },
      {
        role: 'model',
        parts: [
          { text: 'Great to meet you. What would you like to know?' },
        ],
      },
      {
        role: 'user',
        parts: [
          { text: 'I have two dogs in my house. How many paws are in my house?' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:generateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

Streaming can also be used for multi-turn conversations.

PythonJavaScriptGoRESTApps Script 
from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")
chat = client.chats.create(model="gemini-2.0-flash")

response = chat.send_message_stream("I have 2 dogs in my house.")
for chunk in response:
    print(chunk.text, end="")

response = chat.send_message_stream("How many paws are in my house?")
for chunk in response:
    print(chunk.text, end="")

for message in chat.get_history():
    print(f'role - {message.role}', end=": ")
    print(message.parts[0].text)

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const chat = ai.chats.create({
    model: "gemini-2.0-flash",
    history: [
      {
        role: "user",
        parts: [{ text: "Hello" }],
      },
      {
        role: "model",
        parts: [{ text: "Great to meet you. What would you like to know?" }],
      },
    ],
  });

  const stream1 = await chat.sendMessageStream({
    message: "I have 2 dogs in my house.",
  });
  for await (const chunk of stream1) {
    console.log(chunk.text);
    console.log("_".repeat(80));
  }

  const stream2 = await chat.sendMessageStream({
    message: "How many paws are in my house?",
  });
  for await (const chunk of stream2) {
    console.log(chunk.text);
    console.log("_".repeat(80));
  }
}

await main();

package main

import (
  "context"
  "fmt"
  "os"
  "google.golang.org/genai"
)

func main() {

  ctx := context.Background()
  client, _ := genai.NewClient(ctx, &genai.ClientConfig{
      APIKey:  os.Getenv("GEMINI_API_KEY"),
      Backend: genai.BackendGeminiAPI,
  })

  history := []*genai.Content{
      genai.NewContentFromText("Hi nice to meet you! I have 2 dogs in my house.", genai.RoleUser),
      genai.NewContentFromText("Great to meet you. What would you like to know?", genai.RoleModel),
  }

  chat, _ := client.Chats.Create(ctx, "gemini-2.0-flash", nil, history)
  stream := chat.SendMessageStream(ctx, genai.Part{Text: "How many paws are in my house?"})

  for chunk, _ := range stream {
      part := chunk.Candidates[0].Content.Parts[0]
      fmt.Print(part.Text)
  }
}

curl https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?alt=sse&key=$GEMINI_API_KEY \
  -H 'Content-Type: application/json' \
  -X POST \
  -d '{
    "contents": [
      {
        "role": "user",
        "parts": [
          {
            "text": "Hello"
          }
        ]
      },
      {
        "role": "model",
        "parts": [
          {
            "text": "Great to meet you. What would you like to know?"
          }
        ]
      },
      {
        "role": "user",
        "parts": [
          {
            "text": "I have two dogs in my house. How many paws are in my house?"
          }
        ]
      }
    ]
  }'

// See https://developers.google.com/apps-script/guides/properties
// for instructions on how to set the API key.
const apiKey = PropertiesService.getScriptProperties().getProperty('GEMINI_API_KEY');

function main() {
  const payload = {
    contents: [
      {
        role: 'user',
        parts: [
          { text: 'Hello' },
        ],
      },
      {
        role: 'model',
        parts: [
          { text: 'Great to meet you. What would you like to know?' },
        ],
      },
      {
        role: 'user',
        parts: [
          { text: 'I have two dogs in my house. How many paws are in my house?' },
        ],
      },
    ],
  };

  const url = `https://generativelanguage.googleapis.com/v1beta/models/gemini-2.0-flash:streamGenerateContent?key=${apiKey}`;
  const options = {
    method: 'POST',
    contentType: 'application/json',
    payload: JSON.stringify(payload)
  };

  const response = UrlFetchApp.fetch(url, options);
  const data = JSON.parse(response);
  const content = data['candidates'][0]['content']['parts'][0]['text'];
  console.log(content);
}

Supported models

All models in the Gemini family support text generation. To learn more about the models and their capabilities, visit the Models page.

Best practices

Prompting tips

For basic text generation, a zero-shot prompt often suffices without needing examples, system instructions or specific formatting.

For more tailored outputs:

  • Use System instructions to guide the model.
  • Provide few example inputs and outputs to guide the model. This is often referred to as few-shot prompting.
  • Consider fine-tuning for advanced use cases.

Consult our prompt engineering guide for more tips.

Structured output

In some cases, you may need structured output, such as JSON. Refer to our structured output guide to learn how.

What's next