在推出 Gemini 2.0 模型系列时，我们还发布了一组新的 Google Gen AI SDK，以便与 Gemini API 搭配使用：
这些更新后的 SDK 将与所有 Gemini API 模型和功能完全兼容，包括最近添加的 Live API 和 Veo 等。
我们建议您开始将项目从旧版 Gemini SDK 迁移到新版 Gen AI SDK。本指南提供了迁移后的代码示例，以帮助您上手。我们会继续在此处添加示例，帮助您快速上手使用新 SDK。
安装 SDK
之前
Python
pip install -U -q "google-generativeai"
JavaScript
npm install @google/generative-ai
之后
Python
pip install -U -q "google-genai"
JavaScript
npm install @google/genai
身份验证
使用 API 密钥进行身份验证。您可以在 Google AI Studio 中创建 API 密钥。
之前
Python
旧版 SDK 会隐式处理 API 客户端对象。在新 SDK 中，您可以创建 API 客户端并使用它调用 API。请注意，无论是哪种情况，如果您未将 API 密钥传递给客户端，SDK 都会从 GOOGLE_API_KEY 环境变量中提取 API 密钥。
import google.generativeai as genai
genai.configure(api_key=...)
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
之后
Python
export GOOGLE_API_KEY="YOUR_API_KEY"
from google import genai
client = genai.Client() # Set the API key using the GOOGLE_API_KEY env var.
# Alternatively, you could set the API key explicitly:
# client = genai.Client(api_key="your_api_key")
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({apiKey: "GEMINI_API_KEY"});
生成内容
之前
Python
新版 SDK 通过 Client 对象提供对所有 API 方法的访问权限。除了一些有状态的特殊情况（chat 和实时 API session）外，这些都是无状态函数。为了实用性和统一性，返回的对象是 pydantic 类。
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content(
'Tell me a story in 300 words'
)
print(response.text)
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI(process.env.API_KEY);
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const prompt = "Tell me a story in 300 words";
const result = await model.generateContent(prompt);
console.log(result.response.text());
之后
Python
from google import genai
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='Tell me a story in 300 words.'
)
print(response.text)
print(response.model_dump_json(
exclude_none=True, indent=4))
JavaScript
import { GoogleGenAI } from "@google/genai";
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const response = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: "Tell me a story in 300 words.",
});
console.log(response.text);
之前
Python
新版 SDK 中也提供了许多相同的实用功能。例如，系统会自动转换 PIL.Image 对象。
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content([
'Tell me a story based on this image',
Image.open(image_path)
])
print(response.text)
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
function fileToGenerativePart(path, mimeType) {
return {
inlineData: {
data: Buffer.from(fs.readFileSync(path)).toString("base64"),
mimeType,
},
};
}
const prompt = "Tell me a story based on this image";
const imagePart = fileToGenerativePart(
`path/to/organ.jpg`,
"image/jpeg",
);
const result = await model.generateContent([prompt, imagePart]);
console.log(result.response.text());
之后
Python
from google import genai
from PIL import Image
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents=[
'Tell me a story based on this image',
Image.open(image_path)
]
)
print(response.text)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const organ = await ai.files.upload({
file: "path/to/organ.jpg",
});
const response = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: [
createUserContent([
"Tell me a story based on this image",
createPartFromUri(organ.uri, organ.mimeType)
]),
],
});
console.log(response.text);
流式
之前
Python
import google.generativeai as genai
response = model.generate_content(
"Write a cute story about cats.",
stream=True)
for chunk in response:
print(chunk.text)
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const prompt = "Write a story about a magic backpack.";
const result = await model.generateContentStream(prompt);
// Print text as it comes in.
for await (const chunk of result.stream) {
const chunkText = chunk.text();
process.stdout.write(chunkText);
}
之后
Python
from google import genai
client = genai.Client()
for chunk in client.models.generate_content_stream(
model='gemini-2.0-flash',
contents='Tell me a story in 300 words.'
):
print(chunk.text)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const response = await ai.models.generateContentStream({
model: "gemini-2.0-flash",
contents: "Write a story about a magic backpack.",
});
let text = "";
for await (const chunk of response) {
console.log(chunk.text);
text += chunk.text;
}
可选参数
之前
Python
对于新版 SDK 中的所有方法，必需的参数都以关键字参数的形式提供。所有可选输入均在 config 参数中提供。配置参数可以指定为 google.genai.types 命名空间中的 Python 字典或 Config 类。为了提高实用性和统一性，types 模块中的所有定义都是 pydantic 类。
import google.generativeai as genai
model = genai.GenerativeModel(
'gemini-1.5-flash',
system_instruction='you are a story teller for kids under 5 years old',
generation_config=genai.GenerationConfig(
max_output_tokens=400,
top_k=2,
top_p=0.5,
temperature=0.5,
response_mime_type='application/json',
stop_sequences=['\n'],
)
)
response = model.generate_content('tell me a story in 100 words')
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({
model: "gemini-1.5-flash",
generationConfig: {
candidateCount: 1,
stopSequences: ["x"],
maxOutputTokens: 20,
temperature: 1.0,
},
});
const result = await model.generateContent(
"Tell me a story about a magic backpack.",
);
console.log(result.response.text())
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='Tell me a story in 100 words.',
config=types.GenerateContentConfig(
system_instruction='you are a story teller for kids under 5 years old',
max_output_tokens= 400,
top_k= 2,
top_p= 0.5,
temperature= 0.5,
response_mime_type= 'application/json',
stop_sequences= ['\n'],
seed=42,
),
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const response = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: "Tell me a story about a magic backpack.",
config: {
candidateCount: 1,
stopSequences: ["x"],
maxOutputTokens: 20,
temperature: 1.0,
},
});
console.log(response.text);
安全设置
使用安全设置生成回答：
之前
Python
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content(
'say something bad',
safety_settings={
'HATE': 'BLOCK_ONLY_HIGH',
'HARASSMENT': 'BLOCK_ONLY_HIGH',
}
)
JavaScript
import { GoogleGenerativeAI, HarmCategory, HarmBlockThreshold } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({
model: "gemini-1.5-flash",
safetySettings: [
{
category: HarmCategory.HARM_CATEGORY_HARASSMENT,
threshold: HarmBlockThreshold.BLOCK_LOW_AND_ABOVE,
},
],
});
const unsafePrompt =
"I support Martians Soccer Club and I think " +
"Jupiterians Football Club sucks! Write an ironic phrase telling " +
"them how I feel about them.";
const result = await model.generateContent(unsafePrompt);
try {
result.response.text();
} catch (e) {
console.error(e);
console.log(result.response.candidates[0].safetyRatings);
}
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='say something bad',
config=types.GenerateContentConfig(
safety_settings= [
types.SafetySetting(
category='HARM_CATEGORY_HATE_SPEECH',
threshold='BLOCK_ONLY_HIGH'
),
]
),
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const unsafePrompt =
"I support Martians Soccer Club and I think " +
"Jupiterians Football Club sucks! Write an ironic phrase telling " +
"them how I feel about them.";
const response = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: unsafePrompt,
config: {
safetySettings: [
{
category: "HARM_CATEGORY_HARASSMENT",
threshold: "BLOCK_ONLY_HIGH",
},
],
},
});
console.log("Finish reason:", response.candidates[0].finishReason);
console.log("Safety ratings:", response.candidates[0].safetyRatings);
异步
之前
Python
如需将新 SDK 与 asyncio 搭配使用，client.aio 下每种方法都有单独的 async 实现。
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content_async(
'tell me a story in 100 words'
)
之后
Python
from google import genai
client = genai.Client()
response = await client.aio.models.generate_content(
model='gemini-2.0-flash',
contents='Tell me a story in 300 words.'
)
聊天
发起聊天并向模型发送消息：
之前
Python
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
chat = model.start_chat()
response = chat.send_message(
"Tell me a story in 100 words")
response = chat.send_message(
"What happened after that?")
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({ model: "gemini-1.5-flash" });
const chat = model.startChat({
history: [
{
role: "user",
parts: [{ text: "Hello" }],
},
{
role: "model",
parts: [{ text: "Great to meet you. What would you like to know?" }],
},
],
});
let result = await chat.sendMessage("I have 2 dogs in my house.");
console.log(result.response.text());
result = await chat.sendMessage("How many paws are in my house?");
console.log(result.response.text());
之后
Python
from google import genai
client = genai.Client()
chat = client.chats.create(model='gemini-2.0-flash')
response = chat.send_message(
message='Tell me a story in 100 words')
response = chat.send_message(
message='What happened after that?')
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
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);
函数调用
之前
Python
在新版 SDK 中，自动函数调用是默认设置。您可以在此处停用此功能。
import google.generativeai as genai
from enum import Enum
def get_current_weather(location: str) -> str:
"""Get the current whether in a given location.
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-1.5-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
之后
Python
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.
Args:
location: required, The city and state, e.g. San Franciso, CA
unit: celsius or fahrenheit
"""
print(f'Called with: {location=}')
return "23C"
response = client.models.generate_content(
model='gemini-2.0-flash',
contents="What is the weather like in Boston?",
config=types.GenerateContentConfig(
tools=[get_current_weather],
automatic_function_calling={'disable': True},
),
)
function_call = response.candidates[0].content.parts[0].function_call
自动函数调用
之前
Python
旧版 SDK 仅支持在聊天中自动调用函数。在新版 SDK 中，这是 generate_content 的默认行为。
import google.generativeai as genai
def get_current_weather(city: str) -> str:
return "23C"
model = genai.GenerativeModel(
model_name="gemini-1.5-flash",
tools=[get_current_weather]
)
chat = model.start_chat(
enable_automatic_function_calling=True)
result = chat.send_message("What is the weather in San Francisco?")
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
def get_current_weather(city: str) -> str:
return "23C"
response = client.models.generate_content(
model='gemini-2.0-flash',
contents="What is the weather like in Boston?",
config=types.GenerateContentConfig(
tools=[get_current_weather]
),
)
代码执行
代码执行工具可让模型生成 Python 代码、运行该代码并返回结果。
之前
Python
import google.generativeai as genai
model = genai.GenerativeModel(
model_name="gemini-1.5-flash",
tools="code_execution"
)
result = model.generate_content(
"What is the sum of the first 50 prime numbers? Generate and run code for "
"the calculation, and make sure you get all 50.")
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({
model: "gemini-1.5-flash",
tools: [{ codeExecution: {} }],
});
const result = await model.generateContent(
"What is the sum of the first 50 prime numbers? " +
"Generate and run code for the calculation, and make sure you get " +
"all 50.",
);
console.log(result.response.text());
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='What is the sum of the first 50 prime numbers? Generate and run '
'code for the calculation, and make sure you get all 50.',
config=types.GenerateContentConfig(
tools=[types.Tool(code_execution=types.ToolCodeExecution)],
),
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const response = await ai.models.generateContent({
model: "gemini-2.0-pro-exp-02-05",
contents: `Write and execute code that calculates the sum of the first 50 prime numbers.
Ensure that only the executable code and its resulting output are generated.`,
});
// Each part may contain text, executable code, or an execution result.
for (const part of response.candidates[0].content.parts) {
console.log(part);
console.log("\n");
}
console.log("-".repeat(80));
// The `.text` accessor concatenates the parts into a markdown-formatted text.
console.log("\n", response.text);
搜索接地
GoogleSearch（Gemini>=2.0）和 GoogleSearchRetrieval（Gemini < 2.0）是可让模型检索公开 Web 数据以进行“接地”的工具，由 Google 提供支持。
之前
Python
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content(
contents="what is the Google stock price?",
tools='google_search_retrieval'
)
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='What is the Google stock price?',
config=types.GenerateContentConfig(
tools=[
types.Tool(
google_search=types.GoogleSearch()
)
]
)
)
JSON 响应
以 JSON 格式生成回答。
之前
Python
通过指定 response_schema 并设置 response_mime_type="application/json"，用户可以约束模型，使其按照给定结构生成 JSON 响应。新版 SDK 使用 pydantic 类提供架构（不过，您也可以传递 genai.types.Schema 或等效的 dict）。在可能的情况下，SDK 会解析返回的 JSON，并在 response.parsed 中返回结果。如果您将 pydantic 类作为架构提供，SDK 将将该 JSON 转换为该类的实例。
import google.generativeai as genai
import typing_extensions as typing
class CountryInfo(typing.TypedDict):
name: str
population: int
capital: str
continent: str
major_cities: list[str]
gdp: int
official_language: str
total_area_sq_mi: int
model = genai.GenerativeModel(model_name="gemini-1.5-flash")
result = model.generate_content(
"Give me information of the United States",
generation_config=genai.GenerationConfig(
response_mime_type="application/json",
response_schema = CountryInfo
),
)
JavaScript
import { GoogleGenerativeAI, SchemaType } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const schema = {
description: "List of recipes",
type: SchemaType.ARRAY,
items: {
type: SchemaType.OBJECT,
properties: {
recipeName: {
type: SchemaType.STRING,
description: "Name of the recipe",
nullable: false,
},
},
required: ["recipeName"],
},
};
const model = genAI.getGenerativeModel({
model: "gemini-1.5-pro",
generationConfig: {
responseMimeType: "application/json",
responseSchema: schema,
},
});
const result = await model.generateContent(
"List a few popular cookie recipes.",
);
console.log(result.response.text());
之后
Python
from google import genai
from pydantic import BaseModel
client = genai.Client()
class CountryInfo(BaseModel):
name: str
population: int
capital: str
continent: str
major_cities: list[str]
gdp: int
official_language: str
total_area_sq_mi: int
response = client.models.generate_content(
model='gemini-2.0-flash',
contents='Give me information of the United States.',
config={
'response_mime_type': 'application/json',
'response_schema': CountryInfo,
},
)
response.parsed
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const response = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: "List a few popular cookie recipes.",
config: {
responseMimeType: "application/json",
responseSchema: {
type: "array",
items: {
type: "object",
properties: {
recipeName: { type: "string" },
ingredients: { type: "array", items: { type: "string" } },
},
required: ["recipeName", "ingredients"],
},
},
},
});
console.log(response.text);
文件
上传
上传文件：
之前
Python
import requests
import pathlib
import google.generativeai as genai
# Download file
response = requests.get(
'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)
file = genai.upload_file(path='a11.txt')
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.generate_content([
'Can you summarize this file:',
my_file
])
print(response.text)
之后
Python
import requests
import pathlib
from google import genai
client = genai.Client()
# Download file
response = requests.get(
'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)
my_file = client.files.upload(file='a11.txt')
response = client.models.generate_content(
model='gemini-2.0-flash',
contents=[
'Can you summarize this file:',
my_file
]
)
print(response.text)
列出和获取
列出已上传的文件并获取具有文件名的已上传文件：
之前
Python
import google.generativeai as genai
for file in genai.list_files():
print(file.name)
file = genai.get_file(name=file.name)
之后
Python
from google import genai
client = genai.Client()
for file in client.files.list():
print(file.name)
file = client.files.get(name=file.name)
删除
删除文件：
之前
Python
import pathlib
import google.generativeai as genai
pathlib.Path('dummy.txt').write_text(dummy)
dummy_file = genai.upload_file(path='dummy.txt')
file = genai.delete_file(name=dummy_file.name)
之后
Python
import pathlib
from google import genai
client = genai.Client()
pathlib.Path('dummy.txt').write_text(dummy)
dummy_file = client.files.upload(file='dummy.txt')
response = client.files.delete(name=dummy_file.name)
上下文缓存
借助上下文缓存，用户可以将内容传递给模型一次，缓存输入 token，然后在后续调用中引用缓存的 token 以降低费用。
之前
Python
import requests
import pathlib
import google.generativeai as genai
from google.generativeai import caching
# Download file
response = requests.get(
'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)
# Upload file
document = genai.upload_file(path="a11.txt")
# Create cache
apollo_cache = caching.CachedContent.create(
model="gemini-1.5-flash-001",
system_instruction="You are an expert at analyzing transcripts.",
contents=[document],
)
# Generate response
apollo_model = genai.GenerativeModel.from_cached_content(
cached_content=apollo_cache
)
response = apollo_model.generate_content("Find a lighthearted moment from this transcript")
JavaScript
import { GoogleAICacheManager, GoogleAIFileManager } from "@google/generative-ai/server";
import { GoogleGenerativeAI } from "@google/generative-ai";
const cacheManager = new GoogleAICacheManager("GOOGLE_API_KEY");
const fileManager = new GoogleAIFileManager("GOOGLE_API_KEY");
const uploadResult = await fileManager.uploadFile("path/to/a11.txt", {
mimeType: "text/plain",
});
const cacheResult = await cacheManager.create({
model: "models/gemini-1.5-flash",
contents: [
{
role: "user",
parts: [
{
fileData: {
fileUri: uploadResult.file.uri,
mimeType: uploadResult.file.mimeType,
},
},
],
},
],
});
console.log(cacheResult);
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModelFromCachedContent(cacheResult);
const result = await model.generateContent(
"Please summarize this transcript.",
);
console.log(result.response.text());
之后
Python
import requests
import pathlib
from google import genai
from google.genai import types
client = genai.Client()
# Check which models support caching.
for m in client.models.list():
for action in m.supported_actions:
if action == "createCachedContent":
print(m.name)
break
# Download file
response = requests.get(
'https://storage.googleapis.com/generativeai-downloads/data/a11.txt')
pathlib.Path('a11.txt').write_text(response.text)
# Upload file
document = client.files.upload(file='a11.txt')
# Create cache
model='gemini-1.5-flash-001'
apollo_cache = client.caches.create(
model=model,
config={
'contents': [document],
'system_instruction': 'You are an expert at analyzing transcripts.',
},
)
# Generate response
response = client.models.generate_content(
model=model,
contents='Find a lighthearted moment from this transcript',
config=types.GenerateContentConfig(
cached_content=apollo_cache.name,
)
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const filePath = path.join(media, "a11.txt");
const document = await ai.files.upload({
file: filePath,
config: { mimeType: "text/plain" },
});
console.log("Uploaded file name:", document.name);
const modelName = "gemini-1.5-flash";
const contents = [
createUserContent(createPartFromUri(document.uri, document.mimeType)),
];
const cache = await ai.caches.create({
model: modelName,
config: {
contents: contents,
systemInstruction: "You are an expert analyzing transcripts.",
},
});
console.log("Cache created:", cache);
const response = await ai.models.generateContent({
model: modelName,
contents: "Please summarize this transcript",
config: { cachedContent: cache.name },
});
console.log("Response text:", response.text);
统计词元数
统计请求中的令牌数量。
之前
Python
import google.generativeai as genai
model = genai.GenerativeModel('gemini-1.5-flash')
response = model.count_tokens(
'The quick brown fox jumps over the lazy dog.')
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY+);
const model = genAI.getGenerativeModel({
model: "gemini-1.5-flash",
});
// Count tokens in a prompt without calling text generation.
const countResult = await model.countTokens(
"The quick brown fox jumps over the lazy dog.",
);
console.log(countResult.totalTokens); // 11
const generateResult = await model.generateContent(
"The quick brown fox jumps over the lazy dog.",
);
// On the response for `generateContent`, use `usageMetadata`
// to get separate input and output token counts
// (`promptTokenCount` and `candidatesTokenCount`, respectively),
// as well as the combined token count (`totalTokenCount`).
console.log(generateResult.response.usageMetadata);
// candidatesTokenCount and totalTokenCount depend on response, may vary
// { promptTokenCount: 11, candidatesTokenCount: 124, totalTokenCount: 135 }
之后
Python
from google import genai
client = genai.Client()
response = client.models.count_tokens(
model='gemini-2.0-flash',
contents='The quick brown fox jumps over the lazy dog.',
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const prompt = "The quick brown fox jumps over the lazy dog.";
const countTokensResponse = await ai.models.countTokens({
model: "gemini-2.0-flash",
contents: prompt,
});
console.log(countTokensResponse.totalTokens);
const generateResponse = await ai.models.generateContent({
model: "gemini-2.0-flash",
contents: prompt,
});
console.log(generateResponse.usageMetadata);
生成图片
生成图片：
之前
Python
#pip install https://github.com/google-gemini/generative-ai-python@imagen
import google.generativeai as genai
imagen = genai.ImageGenerationModel(
"imagen-3.0-generate-001")
gen_images = imagen.generate_images(
prompt="Robot holding a red skateboard",
number_of_images=1,
safety_filter_level="block_low_and_above",
person_generation="allow_adult",
aspect_ratio="3:4",
)
之后
Python
from google import genai
client = genai.Client()
gen_images = client.models.generate_images(
model='imagen-3.0-generate-001',
prompt='Robot holding a red skateboard',
config=types.GenerateImagesConfig(
number_of_images= 1,
safety_filter_level= "BLOCK_LOW_AND_ABOVE",
person_generation= "ALLOW_ADULT",
aspect_ratio= "3:4",
)
)
for n, image in enumerate(gen_images.generated_images):
pathlib.Path(f'{n}.png').write_bytes(
image.image.image_bytes)
嵌入内容
生成内容嵌入。
之前
Python
import google.generativeai as genai
response = genai.embed_content(
model='models/text-embedding-004',
content='Hello world'
)
JavaScript
import { GoogleGenerativeAI } from "@google/generative-ai";
const genAI = new GoogleGenerativeAI("GOOGLE_API_KEY");
const model = genAI.getGenerativeModel({
model: "text-embedding-004",
});
const result = await model.embedContent("Hello world!");
console.log(result.embedding);
之后
Python
from google import genai
client = genai.Client()
response = client.models.embed_content(
model='text-embedding-004',
contents='Hello world',
)
JavaScript
import {GoogleGenAI} from '@google/genai';
const ai = new GoogleGenAI({ apiKey: "GOOGLE_API_KEY" });
const text = "Hello World!";
const result = await ai.models.embedContent({
model: "text-embedding-004",
contents: text,
config: { outputDimensionality: 10 },
});
console.log(result.embeddings);
调整模型
创建和使用已调参模型。
新版 SDK 通过 client.tunings.tune 简化了调整流程，该函数会启动调整作业并轮询，直到作业完成。
之前
Python
import google.generativeai as genai
import random
# create tuning model
train_data = {}
for i in range(1, 6):
key = f'input {i}'
value = f'output {i}'
train_data[key] = value
name = f'generate-num-{random.randint(0,10000)}'
operation = genai.create_tuned_model(
source_model='models/gemini-1.5-flash-001-tuning',
training_data=train_data,
id = name,
epoch_count = 5,
batch_size=4,
learning_rate=0.001,
)
# wait for tuning complete
tuningProgress = operation.result()
# generate content with the tuned model
model = genai.GenerativeModel(model_name=f'tunedModels/{name}')
response = model.generate_content('55')
之后
Python
from google import genai
from google.genai import types
client = genai.Client()
# Check which models are available for tuning.
for m in client.models.list():
for action in m.supported_actions:
if action == "createTunedModel":
print(m.name)
break
# create tuning model
training_dataset=types.TuningDataset(
examples=[
types.TuningExample(
text_input=f'input {i}',
output=f'output {i}',
)
for i in range(5)
],
)
tuning_job = client.tunings.tune(
base_model='models/gemini-1.5-flash-001-tuning',
training_dataset=training_dataset,
config=types.CreateTuningJobConfig(
epoch_count= 5,
batch_size=4,
learning_rate=0.001,
tuned_model_display_name="test tuned model"
)
)
# generate content with the tuned model
response = client.models.generate_content(
model=tuning_job.tuned_model.model,
contents='55',
)
浏览器中的 JavaScript
如需开始在浏览器中使用 Gemini API，您可以从 CDN 导入适用于 JavaScript 的 Gen AI SDK，如以下示例所示：
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>Using My Package</title>
</head>
<body>
<script type="module">
import {GoogleGenAI} from 'https://cdn.jsdelivr.net/npm/@google/genai@latest/+esm'
const ai = new GoogleGenAI({apiKey: "GOOGLE_API_KEY"});
async function main() {
const response = await ai.models.generateContent({
model: 'gemini-2.0-flash-001',
contents: 'Why is the sky blue?',
});
console.log(response.text);
}
main();
</script>
</body>
</html>
如需在本地运行此代码，您应使用 http-server 等服务器。如果您尝试从本地文件系统运行代码，可能会遇到 CORS 错误。