If your image is stored in your local file system, you can read it in as bytes data and use the from_bytes class method to create a Part object. from google.genai import types YOUR_IMAGE_PATH = 'your_image_path' YOUR_IMAGE_MIME_TYPE = 'your_image_mime_type' with open(YOUR_IMAGE_PATH, 'rb') as f: image_bytes = f.read() for chunk in client.models.generate_content_stream( model='gemini-2.5-flash', contents=[ 'What is this image about?', types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE), ], ): print(chunk.text, end='')

* * <p>mvn clean compile exec:java -Dexec.mainClass="com.google.genai.examples.GenerateContent" * -Dexec.args="YOUR_MODEL_ID" */ package com.google.genai.examples; import com.google.genai.Client; import com.google.genai.types.GenerateContentResponse; /** An example of using the Unified Gen AI Java SDK to generate content.

May 5, 2026 - package <your package name>; import com.google.common.collect.ImmutableList; import com.google.genai.Client; import com.google.genai.types.Content; import com.google.genai.types.GenerateContentResponse; import com.google.genai.types.Part; public class GenerateContentWithImageInput { public static void main(String[] args) { // Instantiate the client using Gemini Enterprise Agent Platform API.

2 weeks ago - If your image is stored in your local file system, you can read it in as bytes data and use the from_bytes class method to create a Part object. from google.genai import types YOUR_IMAGE_PATH = 'your_image_path' YOUR_IMAGE_MIME_TYPE = 'your_image_mime_type' with open(YOUR_IMAGE_PATH, 'rb') as f: image_bytes = f.read() for chunk in client.models.generate_content_stream( model='gemini-3.5-flash', contents=[ 'What is this image about?', types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE), ], ): print(chunk.text, end='')

1 month ago - If your image is stored in your local file system, you can read it in as bytes data and use the from_bytes class method to create a Part object. from google.genai import types YOUR_IMAGE_PATH = 'your_image_path' YOUR_IMAGE_MIME_TYPE = 'your_image_mime_type' with open(YOUR_IMAGE_PATH, 'rb') as f: image_bytes = f.read() for chunk in client.models.generate_content_stream( model='gemini-3.5-flash', contents=[ 'What is this image about?', types.Part.from_bytes(data=image_bytes, mime_type=YOUR_IMAGE_MIME_TYPE), ], ): print(chunk.text, end='')

For tuned models, the model name starts with ‘tunedModels/’, for example: ‘tunedModels/1234567890123456789’ · Some models support multimodal input and output. Built-in MCP support is an experimental feature. ... from google.genai import types from google import genai client = genai.Client( vertexai=True, project='my-project-id', location='us-central1' ) async for chunk in await client.aio.models.generate_content_stream( model='gemini-2.0-flash', contents='''What is a good name for a flower shop that specializes in selling bouquets of dried flowers?''' ): print(chunk.text) # **Elegant

Google Gen AI Python SDK provides an interface for developers to integrate Google's generative models into their Python applications. - python-genai/google/genai/types.py at main · googleapis/python-genai

1 week ago - NOTE: This doesn't apply to FunctionCall and FunctionResponse parts, if you are specifying those, you need to explicitly provide the full Content[] structure making it explicit which Parts are 'spoken' by the model, or the user. The SDK will throw an exception if you try this. To handle errors raised by the API, the SDK provides this ApiError class. import {GoogleGenAI} from '@google/genai'; const GEMINI_API_KEY = process.env.GEMINI_API_KEY; const ai = new GoogleGenAI({apiKey: GEMINI_API_KEY}); async function main() { await ai.models.generateContent({ model: 'non-existent-model', contents: 'Write a 100-word poem.', }).catch((e) => { console.error('error name: ', e.name); console.error('error message: ', e.message); console.error('error status: ', e.status); }); } main();

This is particularly useful for dynamic content or when testing different inputs: from google import genai import instructor from pydantic import BaseModel from google.genai import types # Define your Pydantic model class User(BaseModel): name: str age: int # Initialize and patch the client client = instructor.from_provider("google/gemini-2.5-flash") # Single string (converted to user message) response = client.create( messages=[{"role": "user", "content": "{{ name }} is {{ age }} years old"}], response_model=User, context={ "name": "Jason", "age": 25, }, ) print(response) # > name='Jason' age

3 weeks ago - This SDK supports use cases like: ... For example, with just a few lines of code, you can access Gemini's multimodal capabilities to generate text from text-and-image input. parts := []*genai.Part{ {Text: "What's this image about?"}, {InlineData: &genai.Blob{Data: imageBytes, MIMEType: ...

Attributes: mime_type (str): The IANA standard MIME type of the source data. Examples: - image/png - image/jpeg If an unsupported MIME type is provided, an error will be returned. For a complete list of supported types, see `Supported file formats <https://ai.google.dev/gemini-api/docs/prompting_with_media#supported_file_formats>`__.

March 3, 2026 - A FunctionResponsePart must have a fixed IANA MIME type identifying the type and subtype of the media if the inlineData field is filled with raw bytes. ... The data of the function response part.

using System.Threading.Tasks; using Google.GenAI; using Google.GenAI.Types; public class CreateCache { public static async Task main() { // Example for Gemini Enterprise Agent Platform with GCS URIs: var enterpriseClient = new Client(project: project, location: location, enterprise: true); var agenterpriseConfig = new CreateCachedContentConfig { Contents = new List<Content> { new Content { Role = "user", Parts = new List<Part> { new Part { FileData = new FileData { FileUri = "gs://cloud-samples-data/generative-ai/pdf/2312.11805v3.pdf", MimeType = "application/pdf" } }, new Part { FileData = ne

May 20, 2026 - What would you like to know?", genai.RoleModel), } chat, err := client.Chats.Create(ctx, "gemini-3.5-flash", nil, history) if err != nil { log.Fatal(err) } firstResp, err := chat.SendMessage(ctx, genai.Part{Text: "I have 2 dogs in my house."}) if err != nil { log.Fatal(err) } fmt.Println(firstResp.Text()) secondResp, err := chat.SendMessage(ctx, genai.Part{Text: "How many paws are in my house?"}) if err != nil { log.Fatal(err) } fmt.Println(secondResp.Text()) chat.go · 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.

While the simpler API call is often sufficient, you may run into scenarios where you need to work directly with the underlying Content and Part objects for more explicit control. These are the fundamental building blocks of the generate_content API. For instance, the following simple API call: from google import genai client = genai.Client() response = client.models.generate_content( model='gemini-3-flash-preview', contents='How does AI work?' ) print(response.text) is effectively a shorthand for this more explicit structure: from google import genai from google.genai import types client = genai.Client() response = client.models.generate_content( model='gemini-3-flash-preview', contents=[ types.Content(role='user', parts=[types.Part.from_text(text='How does AI work?')]), ] ) print(response.text) The list of APIs and capabilities above are not comprehensive.

April 28, 2026 - Args: location: required, The city and state, e.g. San Franciso, CA unit: celsius or fahrenheit """ print(f'Called with: {location=}') return "23C" model = genai.GenerativeModel( model_name="gemini-3.5-flash", tools=[get_current_weather] ) response = model.generate_content("What is the weather in San Francisco?") function_call = response.candidates[0].parts[0].function_call · After · In the new SDK, automatic function calling is the default. Here, you disable it. from google import genai from google.genai import types client = genai.Client() def get_current_weather(location: str) -> str: """Get the current whether in a given location.

from google import genai from google.genai.types import HttpOptions client = genai.Client(http_options=HttpOptions(api_version="v1")) response = client.models.generate_content( model="gemini-3.5-flash", contents="How does AI work?", ) print(response.text) # Example response: # Okay, let's break down how AI works.

prompt = { "prompt_data": { "contents": [{"parts": [{"text": "Hello, {name}! How are you?"}]}], "system_instruction": {"parts": [{"text": "Please answer in a short sentence."}]}, "variables": [ {"name": {"text": "Alice"}}, ], "model": "gemini-2.5-flash", }, } prompt_resource = client.prompts.create( prompt=prompt, ) Note that you can also use the types.Prompt object to define your prompt. Some of the types used to do this are from the Gen AI SDK. import types from google.genai import types as genai_types prompt = types.Prompt( prompt_data=types.PromptData( contents=[genai_types.Content(parts=[genai_types.Part(text="Hello, {name}! How are you?")])], system_instruction=genai_types.Content(parts=[genai_types.Part(text="Please answer in a short sentence.")]), variables=[ {"name": genai_types.Part(text="Alice")}, ], model="gemini-2.5-flash", ), )

May 2, 2025 - FunctionCalls return all the FunctionCall parts in the candidate. type ChatSession struct { History []*Content // contains filtered or unexported fields } A ChatSession provides interactive chat. ... This example shows how to set the History field on ChatSession explicitly. package main import ( "context" "fmt" "log" "os" "github.com/google/generative-ai-go/genai" "google.golang.org/api/option" ) func main() { ctx := context.Background() client, err := genai.NewClient(ctx, option.WithAPIKey(os.Getenv("GEMINI_API_KEY"))) if err != nil { log.Fatal(err) } defer client.Close() model := client.GenerativeModel("gemini-1.5-flash") cs := model.StartChat() cs.History = []*genai.Content{ { Parts: []genai.Part{ genai.Text("Hello, I have 2 dogs in my house."), }, Role: "user", }, { Parts: []genai.Part{ genai.Text("Great to meet you.

Self-updating documentation for startups, enterprises, and agents.