运用 Gemini 思维

Gemini 2.5 Pro 实验版和 Gemini 2.0 Flash Thinking 实验版模型在生成回答时会使用内部“思考过程”。此过程有助于提高推理能力，让机器人能够解决复杂任务。本指南介绍了如何使用具有思考能力的 Gemini 模型。

使用思维模型

具有思考能力的模型可在 Google AI Studio 中使用，也可通过 Gemini API 使用。请注意，思考过程会显示在 Google AI Studio 中，但不会作为 API 输出的一部分提供。

发送基本请求

Python

from google import genai

client = genai.Client(api_key="GEMINI_API_KEY")
prompt = "Explain the concept of Occam's Razor and provide a simple, everyday example."
response = client.models.generate_content(
    model="gemini-2.5-pro-exp-03-25",  # or gemini-2.0-flash-thinking-exp
    contents=prompt
)

print(response.text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
  const prompt = "Explain the concept of Occam's Razor and provide a simple, everyday example.";

  const response = await ai.models.generateContent({
    model: "gemini-2.5-pro-exp-03-25",  // or gemini-2.0-flash-thinking-exp
    contents: prompt,
  });

  console.log(response.text);
}

main();

Go

// import packages here

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-2.5-pro-exp-03-25")  // or gemini-2.0-flash-thinking-exp
  resp, err := model.GenerateContent(ctx, genai.Text("Explain the concept of Occam's Razor and provide a simple, everyday example."))
  if err != nil {
    log.Fatal(err)
  }
  fmt.Println(resp.Text())
}

REST

curl "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro-exp-03-25:generateContent?key=$YOUR_API_KEY" \
 -H 'Content-Type: application/json' \
 -X POST \
 -d '{
   "contents": [
     {
       "parts": [
         {
           "text": "Explain the concept of Occam\''s Razor and provide a simple, everyday example."
         }
       ]
     }
   ]
 }'
 ```

多轮思考对话

如需考虑之前的聊天记录，您可以使用多轮对话。

借助这些 SDK，您可以创建聊天会话来管理对话状态。

Python

from google import genai

client = genai.Client(api_key='GEMINI_API_KEY')

chat = client.aio.chats.create(
    model='gemini-2.5-pro-exp-03-25',  # or gemini-2.0-flash-thinking-exp
)
response = await chat.send_message('What is your name?')
print(response.text)
response = await chat.send_message('What did you just say before this?')
print(response.text)

JavaScript

import { GoogleGenAI } from "@google/genai";

const ai = new GoogleGenAI({ apiKey: "GEMINI_API_KEY" });

async function main() {
    const chat = ai.chats.create({
        model: 'gemini-2.5-pro-exp-03-25'  // or gemini-2.0-flash-thinking-exp
    });

    const response = await chat.sendMessage({
        message: 'What is your name?'
    });
    console.log(response.text);

    response = await chat.sendMessage({
        message: 'What did you just say before this?'
    });
    console.log(response.text);
}

main();

后续操作