gemini怎么使用？免费额度、接口实操与开发者接入注意事项

# macOS / Linuxexport GEMINI_API_KEY="你的API_KEY"# Windows PowerShell$env:GEMINI_API_KEY="你的API_KEY"# 验证（Linux/macOS）echo $GEMINI_API_KEY

pip install -U google-genai

npm install @google/genai

from google import genaiclient = genai.Client(api_key="你的API_KEY")# Interactions API — 2026年6月起的新默认接口interaction = client.interactions.create(    model="gemini-3.5-flash",    input="用 Python 写一个计算斐波那契数列的函数")print(interaction.output_text)

from google import genaiclient = genai.Client(api_key="你的API_KEY")# generateContent — 经典接口，兼容旧代码response = client.models.generate_content(    model="gemini-3.5-flash",    contents="解释什么是 REST API")print(response.text)

from google import genaiclient = genai.Client(api_key="你的API_KEY")# 流式响应，逐块打印response = client.models.generate_content_stream(    model="gemini-3.5-flash",    contents="详细解释量子计算原理")for chunk in response:    print(chunk.text, end="", flush=True)

from google import genaiclient = genai.Client(api_key="你的API_KEY")# 创建带状态的聊天会话chat = client.chats.create(model="gemini-3.5-flash")r1 = chat.send_message("我有两只狗住在家里")print("助手:", r1.text)r2 = chat.send_message("我家总共有多少只爪子？")print("助手:", r2.text)# 查看历史记录print(chat.history)

from google import genaifrom google.genai import typesclient = genai.Client(api_key="你的API_KEY")# 上传图片并提问response = client.models.generate_content(    model="gemini-3.5-flash",    contents=[        types.Part.from_image(image=types.Image.load_from_file("screenshot.png")),        "这张截图里的报错信息是什么？应该如何修复？"    ])print(response.text)

from google import genaiclient = genai.Client(api_key="你的API_KEY")# 定义函数工具tools = [    {        "function_declarations": [            {                "name": "get_weather",                "description": "获取指定城市的天气",                "parameters": {                    "type": "object",                    "properties": {                        "city": {"type": "string", "description": "城市名称"}                    },                    "required": ["city"]                }            }        ]    }]response = client.models.generate_content(    model="gemini-3.5-flash",    contents="北京今天天气怎么样？",    tools=tools)# 模型返回函数调用请求print(response.function_calls)  # [{name: 'get_weather', args: {city: '北京'}}]

from google import genaiclient = genai.Client(api_key="你的API_KEY")response = client.models.generate_content(    model="gemini-3.5-flash",    contents="把以下信息整理成 JSON：姓名张三、年龄30、职业工程师",    config=client.models.generate_content.config(        response_modalities=["text"],        temperature=0.3,        # 指定 JSON Schema（以 safety_settings 或 config 形式传递）    ))print(response.text)

# 基础文本调用curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:generateContent?key=$GEMINI_API_KEY" \  -H "Content-Type: application/json" \  -X POST \  -d '{"contents":[{"parts":[{"text":"用一句话介绍 Gemini"}]}]}'# 流式响应curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-3.5-flash:streamGenerateContent?key=$GEMINI_API_KEY" \  -H "Content-Type: application/json" \  --no-buffer \  -X POST \  -d '{"contents":[{"parts":[{"text":"写一首关于 AI 的诗"}]}]}'

from openai import OpenAIclient = OpenAI(    api_key="你的GEMINI_API_KEY",    base_url="https://generativelanguage.googleapis.com/v1beta/openai/")response = client.chat.completions.create(    model="gemini-3.5-flash",    messages=[        {"role": "system", "content": "你是一个专业的 Python 导师"},        {"role": "user", "content": "解释 Python 的装饰器是什么"}    ])print(response.choices[0].message.content)