Overview
Content types represent messages exchanged with the model. Each message can contain multiple parts with different media types.
Content
Base class for multi-part message content.
Fields
List of parts that constitute a single message. Each part may have a different IANA MIME type.
The producer of the content. Must be either ‘user’ or ‘model’. Defaults to ‘user’ if not set.
Example
from google.genai.types import Content, Part
content = Content(
parts=[
Part.from_text(text="What's in this image?"),
Part.from_uri(file_uri="gs://bucket/image.jpg", mime_type="image/jpeg")
],
role="user"
)
UserContent
Convenience class for creating user messages.
Constructor
UserContent(parts: Union[PartUnionDict, list[PartUnionDict], list[Part]])
role="user" and cannot be changed.
Examples
from google.genai.types import UserContent, Part
# Simple text
user_content = UserContent("Why is the sky blue?")
# Single Part
user_content = UserContent(
Part.from_uri(file_uri="gs://bucket/file.txt", mime_type="text/plain")
)
# Multiple parts
user_content = UserContent([
"Analyze this image:",
Part.from_bytes(data=image_bytes, mime_type="image/png")
])
# List of Part objects
parts = [
Part.from_text(text="Question 1"),
Part.from_text(text="Question 2")
]
user_content = UserContent(parts)
ModelContent
Convenience class for creating model messages.
Constructor
ModelContent(parts: Union[PartUnionDict, list[PartUnionDict], list[Part]])
role="model" and cannot be changed.
Examples
from google.genai.types import ModelContent, Part
# Simple text response
model_content = ModelContent("The sky appears blue because...")
# Response with function call
model_content = ModelContent(
Part.from_function_call(name="get_weather", args={"location": "London"})
)
# Multiple response parts
model_content = ModelContent([
"Here's the code:",
Part.from_executable_code(
code="print('Hello')",
language=Language.PYTHON
)
])
Part Union Types
PartUnionDict
Accepts various input formats:
PartUnionDict = Union[
str, # Text string
Part, # Part object
PartDict, # Part dictionary
File, # File object
PIL.Image.Image, # PIL Image
]
ContentUnion
ContentUnion = Union[
str, # Simple text
Content, # Content object
ContentDict, # Content dictionary
list[Part], # List of parts
list[PartUnionDict], # List of part unions
]
Content Construction Patterns
Text Only
# All equivalent
content1 = UserContent("Hello")
content2 = Content(parts=[Part.from_text(text="Hello")], role="user")
content3 = Content(parts=[Part(text="Hello")], role="user")
Multimodal Content
from google.genai.types import UserContent, Part
# Image + text
content = UserContent([
"What's in this image?",
Part.from_uri(
file_uri="gs://bucket/image.jpg",
mime_type="image/jpeg"
)
])
# Video + text with metadata
content = UserContent([
"Describe this video",
Part(
file_data=FileData(
file_uri="gs://bucket/video.mp4",
mime_type="video/mp4"
),
video_metadata=VideoMetadata(
start_offset="00:00:10",
end_offset="00:00:30",
fps=1.0
)
)
])
# Audio + text
content = UserContent([
"Transcribe this audio",
Part.from_bytes(
data=audio_bytes,
mime_type="audio/wav"
)
])
Function Calling
from google.genai.types import ModelContent, UserContent, Part
# Model makes function call
model_call = ModelContent(
Part.from_function_call(
name="get_weather",
args={"location": "San Francisco", "unit": "celsius"}
)
)
# User provides function response
user_response = UserContent(
Part.from_function_response(
name="get_weather",
response={"temperature": 18, "condition": "sunny"}
)
)
Chat History
from google.genai.types import UserContent, ModelContent
history = [
UserContent("What is 2+2?"),
ModelContent("2+2 equals 4."),
UserContent("What about 3+3?"),
ModelContent("3+3 equals 6."),
]
response = client.models.generate_content(
model="gemini-2.0-flash",
contents=history + [UserContent("What about 4+4?")]
)
Dict Representations
ContentDict
from google.genai.types import ContentDict, PartDict
content_dict: ContentDict = {
"parts": [
{"text": "Hello"},
{"inline_data": {"data": b"...", "mime_type": "image/png"}}
],
"role": "user"
}
PartDict
from google.genai.types import PartDict
text_part: PartDict = {"text": "Hello"}
image_part: PartDict = {
"inline_data": {
"data": image_bytes,
"mime_type": "image/jpeg"
}
}
file_part: PartDict = {
"file_data": {
"file_uri": "gs://bucket/file.pdf",
"mime_type": "application/pdf"
}
}
from google.genai.types import Part, PartMediaResolutionLevel
# Using enum
part = Part.from_uri(
file_uri="gs://bucket/image.jpg",
mime_type="image/jpeg",
media_resolution=PartMediaResolutionLevel.MEDIA_RESOLUTION_HIGH
)
# Using string
part = Part.from_uri(
file_uri="gs://bucket/image.jpg",
mime_type="image/jpeg",
media_resolution="MEDIA_RESOLUTION_HIGH"
)
# Using dict with num_tokens
part = Part.from_uri(
file_uri="gs://bucket/video.mp4",
mime_type="video/mp4",
media_resolution={"num_tokens": 512}
)
See Also