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Method Signature

def generate_content(
    self,
    *,
    model: str,
    contents: ContentListUnionDict,
    config: Optional[GenerateContentConfigOrDict] = None,
) -> GenerateContentResponse

async def generate_content(
    self,
    *,
    model: str,
    contents: ContentListUnionDict,
    config: Optional[GenerateContentConfigOrDict] = None,
) -> GenerateContentResponse

​
Description

Makes an API request to generate content using a Gemini model. Supports text-only and multimodal input/output, including images, audio, and video. The method includes built-in support for automatic function calling (AFC) when tools are provided. MCP (Model Context Protocol) support is available as an experimental feature.

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Parameters

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model
str
required
The model to use for generation.Vertex AI formats:
  • Model ID: 'gemini-2.0-flash'
  • Full resource name: 'projects/my-project/locations/us-central1/publishers/google/models/gemini-2.0-flash'
  • Partial resource name: 'publishers/google/models/gemini-2.0-flash'
  • Publisher/model: 'google/gemini-2.0-flash'
Gemini API formats:
  • Model ID: 'gemini-2.0-flash'
  • Model name: 'models/gemini-2.0-flash'
  • Tuned model: 'tunedModels/1234567890123456789'
​
contents
ContentListUnionDict
required
The conversation history or input prompt to generate content from.Can be:
  • A string: 'What is your name?'
  • A list of Content objects
  • A list of Part objects
  • Mixed content with text, images, video, and audio
​
config
GenerateContentConfig
Configuration for content generation.

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Response

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candidates
list[Candidate]
List of generated response candidates
​
usage_metadata
UsageMetadata
Token usage information
​
prompt_feedback
PromptFeedback
Feedback about the prompt (e.g., safety blocks)
​
model_version
str
The model version used
​
text
str
Convenience property: text from the first candidate

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Code Examples

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Basic Text Generation

from google import genai

client = genai.Client(api_key='your-api-key')

response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents='What is a good name for a flower shop?'
)

print(response.text)
# Output: **Elegant & Classic:**
# * The Dried Bloom
# * Everlasting Florals

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Multimodal Generation (Image Input)

from google import genai
from google.genai import types

client = genai.Client(vertexai=True, project='my-project', location='us-central1')

response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents=[
        types.Part.from_text('What is shown in this image?'),
        types.Part.from_uri(
            file_uri='gs://generativeai-downloads/images/scones.jpg',
            mime_type='image/jpeg'
        )
    ]
)

print(response.text)
# Output: The image shows freshly baked blueberry scones.

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Structured JSON Output

response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents='List 3 popular cookie recipes',
    config={
        'response_mime_type': 'application/json',
        'response_schema': {
            'type': 'object',
            'properties': {
                'recipes': {
                    'type': 'array',
                    'items': {
                        'type': 'object',
                        'properties': {
                            'name': {'type': 'string'},
                            'ingredients': {'type': 'array', 'items': {'type': 'string'}}
                        }
                    }
                }
            }
        }
    }
)

print(response.text)
# Output: {"recipes": [{"name": "Chocolate Chip", ...}]}

​
Function Calling (Automatic)

from google.genai import types

def get_weather(location: str) -> str:
    """Get the weather for a location."""
    return f"Sunny, 72°F in {location}"

response = client.models.generate_content(
    model='gemini-2.0-flash',
    contents='What is the weather in San Francisco?',
    config=types.GenerateContentConfig(
        tools=[get_weather],
    )
)

print(response.text)
# The function is automatically called and the result is used
# Output: The weather in San Francisco is sunny and 72°F.

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Async Usage

import asyncio
from google import genai

client = genai.Client(api_key='your-api-key')

async def generate():
    response = await client.aio.models.generate_content(
        model='gemini-2.0-flash',
        contents='Write a haiku about programming'
    )
    print(response.text)

asyncio.run(generate())

​
Notes

  • The method automatically handles function calling when Python callables are provided in tools
  • MCP (Model Context Protocol) sessions are only supported in async methods
  • Use generate_content_stream for streaming responses
  • Multimodal input is supported for Gemini 2.0 and later models
  • Some configuration options are only available on Vertex AI or Gemini API