Interactions API 現已正式發布。建議使用這個 API，存取所有最新功能和模型。

Google 會運用 AI 技術將內容翻譯成你偏好的語言，但可能會出錯。

遷移至 Interactions API

本指南將協助您從 generateContent API 遷移至 Interactions API。

Interactions API 是使用 Gemini 模型和代理程式建構應用程式最簡單且最佳的方式。雖然我們仍會提供 generateContent 的完整支援，但建議所有新開發項目都使用 Interactions API。

為何要遷移？

Interactions API 是使用 Gemini 模型和代理程式建構應用程式最簡單且最有效的方法：

伺服器端記錄管理：透過 previous_interaction_id 簡化多輪對話流程。伺服器預設會啟用狀態 (store=true)，但您可以設定 store=false，選擇無狀態行為。
可觀察的執行步驟：輸入步驟可輕鬆偵錯複雜流程，並為中繼事件 (例如想法或搜尋小工具) 算繪 UI。
工具使用和代理工作流程：透過型別執行步驟，原生支援多步驟工具使用、自動調度管理和複雜的推論流程。
長期執行的工作和背景工作：支援使用 background=true 將耗時的作業 (例如 Deep Think 和 Deep Research) 卸載至背景程序。

基本輸入/輸出

本節說明如何遷移簡單的文字生成要求。

之前 (`generateContent`)

generateContent API 為無狀態，會直接傳回回應。回應結構會將輸出內容包裝在 candidates 清單中，每個 candidates 都包含 content，其中含有要剖析的 parts 清單。

Python

from google import genai

client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash-lite", contents="Tell me a joke."
)
print(response.text)

JavaScript

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

const ai = new GoogleGenAI({});

const response = await ai.models.generateContent({
  model: "gemini-2.5-flash-lite",
  contents: "Tell me a joke.",
});
console.log(response.text);

REST

# Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "contents": [{
        "parts": [{
            "text": "Tell me a joke."
        }]
    }]
}'

# Response
{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "Why did the chicken cross the road? To get to the other side!"
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0
    }
  ],
  "usageMetadata": {
    "promptTokenCount": 4,
    "candidatesTokenCount": 12,
    "totalTokenCount": 16
  }
}

Interactions API 會傳回儲存的互動資源，並附上 steps 時間軸。雖然您可以手動檢查 steps 陣列來尋找中繼事件，但 Google GenAI SDK 會在傳回的 Interaction 物件上直接提供便利屬性，方便您存取最終輸出內容。

最常見的便利性屬性是 .output_text (字串)，可自動擷取並合併模型回覆結尾的連續 TextContent 區塊。雖然這項功能非常適合簡單的回覆，但不會納入以非文字內容 (例如想法、圖片、音訊或工具呼叫) 分隔的先前文字區塊。如要處理複雜或交錯的多模態回應，請改為手動疊代 steps。

Python

from google import genai

client = genai.Client()

interaction = client.interactions.create(
    model="gemini-3.6-flash", input="Tell me a joke."
)

print(interaction.output_text)

JavaScript

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

const client = new GoogleGenAI({});

let interaction = await client.interactions.create({
    model: 'gemini-3.6-flash',
    input: 'Tell me a joke.'
});

console.log(interaction.output_text);

REST

# Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta2/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "model": "gemini-3.6-flash",
    "input": "Tell me a joke."
}'

# Response
{
  "id": "int_123",
  "status": "completed",
  "steps": [
    {
      "type": "user_input",
      "status": "done",
      "content": [
        {
          "type": "text",
          "text": "Tell me a joke."
        }
      ]
    },
    {
      "type": "model_output",
      "status": "done",
      "content": [
        {
          "type": "text",
          "text": "Why did the chicken cross the road?"
        }
      ]
    }
  ]
}

多轉折對話

Interactions API 預設會儲存互動內容，方便您管理多輪對話的伺服器端狀態。

之前 (`generateContent`)

在 generateContent 中，您必須使用 contents 陣列或用戶端即時通訊輔助程式，手動管理對話記錄。

Python

使用即時通訊小幫手 (建議做法)

from google import genai

client = genai.Client()

chat = client.chats.create(model="gemini-2.5-flash-lite")
response1 = chat.send_message("Hi, my name is Phil.")
print(response1.text)

response2 = chat.send_message("What is my name?")
print(response2.text)

手動管理記錄

from google import genai
from google.genai import types

client = genai.Client()

response = client.models.generate_content(
    model="gemini-2.5-flash-lite",
    contents=[
        types.Content(
            role="user", parts=[types.Part.from_text(text="Hi, my name is Phil.")]
        ),
        types.Content(
            role="model",
            parts=[types.Part.from_text(text="Hi Phil, how can I help you?")],
        ),
        types.Content(
            role="user", parts=[types.Part.from_text(text="What is my name?")]
        ),
    ],
)
print(response.text)

JavaScript

使用即時通訊小幫手 (建議做法)

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

const client = new GoogleGenAI({});

const chat = client.chats.create({ model: 'gemini-2.5-flash-lite' });
let response = await chat.sendMessage({ message: 'Hi, my name is Phil.' });
console.log(response.text);

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

手動管理記錄

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

const client = new GoogleGenAI({});

const response = await client.models.generateContent({
    model: 'gemini-2.5-flash-lite',
    contents: [
        { role: 'user', parts: [{ text: 'Hi, my name is Phil.' }] },
        { role: 'model', parts: [{ text: 'Hi Phil, how can I help you?' }] },
        { role: 'user', parts: [{ text: 'What is my name?' }] }
    ]
});
console.log(response.text);

REST

# Request (the second turn requires sending the entire history)
curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "contents": [
        {"role": "user", "parts": [{"text": "Hi, my name is Phil."}]},
        {"role": "model", "parts": [{"text": "Hi Phil, how can I help you?"}]},
        {"role": "user", "parts": [{"text": "What is my name?"}]}
    ]
}'

# Response
{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "Your name is Phil."
          }
        ],
        "role": "model"
      },
      "finishReason": "STOP",
      "index": 0
    }
  ]
}

之後 (Interactions API)

Interactions API 會管理伺服器上的狀態。如要繼續對話，請參照 previous_interaction_id。

Python

from google import genai

client = genai.Client()

interaction1 = client.interactions.create(
    model="gemini-3.6-flash", input="Hi, my name is Phil."
)
print("Response 1:", interaction1.output_text)

interaction2 = client.interactions.create(
    model="gemini-3.6-flash",
    previous_interaction_id=interaction1.id,
    input="What is my name?",
)
print("Response 2:", interaction2.output_text)

JavaScript

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

const client = new GoogleGenAI({});

let interaction = await client.interactions.create({
    model: 'gemini-3.6-flash',
    input: 'Hi, my name is Phil.'
});
console.log("Response 1:", interaction.output_text);

interaction = await client.interactions.create({
    model: 'gemini-3.6-flash',
    previous_interaction_id: interaction.id,
    input: 'What is my name?'
});
console.log("Response 2:", interaction.output_text);

REST

# First Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta2/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "model": "gemini-3.6-flash",
    "input": "Hi, my name is Phil."
}'

# Second Request (using ID from first response)
curl -X POST "https://generativelanguage.googleapis.com/v1beta2/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "model": "gemini-3.6-flash",
    "previous_interaction_id": "int_123",
    "input": "What is my name?"
}'

# Response to Second Request
{
  "id": "int_123",
  "steps": [
    {
      "type": "user_input",
      "status": "done",
      "content": [{ "type": "text", "text": "Hi, my name is Phil." }]
    },
    {
      "type": "model_output",
      "status": "done",
      "content": [{ "type": "text", "text": "Hello Phil! How can I help you today?" }]
    },
    {
      "type": "user_input",
      "status": "done",
      "content": [{ "type": "text", "text": "What is my name?" }]
    },
    {
      "type": "model_output",
      "status": "done",
      "content": [{ "type": "text", "text": "Your name is Phil." }]
    }
  ]
}

多模態輸入內容

這兩個 API 都支援多模態輸入內容 (文字、圖片、影片等)。

之前 (`generateContent`)

在 generateContent 中，您會在 contents 陣列中傳遞 parts 清單。回應會傳回第一個候選人的 parts 輸出內容。

Python

from google import genai
from google.genai import types

client = genai.Client()

with open("sample.jpg", "rb") as f:
    image_bytes = f.read()

response = client.models.generate_content(
    model="gemini-2.5-flash-lite",
    contents=[
        types.Part.from_bytes(data=image_bytes, mime_type="image/jpeg"),
        "Describe this image.",
    ],
)
print(response.text)

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';

const client = new GoogleGenAI({});

const imageBytes = fs.readFileSync('sample.jpg').toString('base64');

const response = await client.models.generateContent({
    model: 'gemini-2.5-flash-lite',
    contents: [
        {
            inlineData: {
                data: imageBytes,
                mimeType: 'image/jpeg',
            },
        },
        'Describe this image.',
    ],
});
console.log(response.text);

REST

# Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "contents": [{
        "parts": [
            {
                "inlineData": {
                    "mimeType": "image/jpeg",
                    "data": "..."
                }
            },
            {
                "text": "Describe this image."
            }
        ]
    }]
}'

# Response
{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "This is a picture of a beautiful sunset."
          }
        ],
        "role": "model"
      }
    }
  ]
}

之後 (Interactions API)

在 Interactions API 中，您會將陣列傳遞至 input 欄位。在時間軸中找到 model_output 步驟，即可擷取輸出內容。

Python

import base64
from google import genai

client = genai.Client()

with open("sample.jpg", "rb") as f:
    image_bytes = f.read()
image_b64 = base64.b64encode(image_bytes).decode("utf-8")

interaction = client.interactions.create(
    model="gemini-3.6-flash",
    input=[
        {
            "type": "image",
            "mime_type": "image/jpeg",
            "data": image_b64,
        },
        {"type": "text", "text": "Describe this image."},
    ],
)
print(interaction.output_text)

JavaScript

import { GoogleGenAI } from '@google/genai';
import * as fs from 'fs';

const client = new GoogleGenAI({});

const imageBytes = fs.readFileSync('sample.jpg').toString('base64');

const interaction = await client.interactions.create({
    model: 'gemini-3.6-flash',
    input: [
        {
            type: 'image',
            mime_type: 'image/jpeg',
            data: imageBytes
        },
        {
            type: 'text',
            text: 'Describe this image.'
        }
    ]
});
console.log(interaction.output_text);

REST

# Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta2/interactions" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "model": "gemini-3.6-flash",
    "input": [
        {
            "type": "image",
            "mime_type": "image/jpeg",
            "data": "..."
        },
        {
            "type": "text",
            "text": "Describe this image."
        }
    ]
}'

# Response
{
  "id": "int_multimodal",
  "steps": [
    {
      "type": "user_input",
      "status": "done",
      "content": [
        {
          "type": "image",
          "mime_type": "image/jpeg",
          "data": "..."
        },
        {
          "type": "text",
          "text": "Describe this image."
        }
      ]
    },
    {
      "type": "model_output",
      "status": "done",
      "content": [
        {
          "type": "text",
          "text": "This is a picture of a beautiful sunset over the mountains."
        }
      ]
    }
  ]
}

結構化輸出內容

如要讓模型傳回符合特定結構定義的 JSON，請設定回覆格式。

之前 (`generateContent`)

在 generateContent 中，您可以使用 config (或 generationConfig) 物件內嵌的 response_mime_type 和 response_schema 欄位設定輸出格式。

Python

from google import genai
from google.genai import types
from pydantic import BaseModel

client = genai.Client()

class Recipe(BaseModel):
    recipe_name: str
    ingredients: list[str]

response = client.models.generate_content(
    model="gemini-2.5-flash-lite",
    contents="Give me a recipe for chocolate chip cookies.",
    config=types.GenerateContentConfig(
        response_mime_type="application/json",
        response_schema=Recipe,
    ),
)
print(response.text)

JavaScript

import { GoogleGenAI, Type } from '@google/genai';

const ai = new GoogleGenAI({});

const response = await ai.models.generateContent({
    model: 'gemini-2.5-flash-lite',
    contents: 'Give me a recipe for chocolate chip cookies.',
    config: {
        responseMimeType: 'application/json',
        responseSchema: {
            type: Type.OBJECT,
            properties: {
                recipe_name: { type: Type.STRING },
                ingredients: {
                    type: Type.ARRAY,
                    items: { type: Type.STRING },
                },
            },
            required: ['recipe_name', 'ingredients'],
        },
    },
});
console.log(response.text);

REST

# Request
curl -X POST "https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-flash-lite:generateContent" \
-H "Content-Type: application/json" \
-H "x-goog-api-key: $GEMINI_API_KEY" \
-d '{
    "contents": [{
        "parts": [{
            "text": "Give me a recipe for chocolate chip cookies."
        }]
    }],
    "generationConfig": {
        "responseMimeType": "application/json",
        "responseSchema": {
            "type": "OBJECT",
            "properties": {
                "recipe_name": { "type": "STRING" },
                "ingredients": {
                    "type": "ARRAY",
                    "items": { "type": "STRING" }
                }
            },
            "required": ["recipe_name", "ingredients"]
        }
    }
}'

# Response
{
  "candidates": [
    {
      "content": {
        "parts": [
          {
            "text": "{\n  \"recipe_name\": \"Chocolate Chip Cookies\",\n  \"ingredients\": [\n    \"1 cup butter\",\n    \"1 cup sugar\",\n    \"2 cups flour\",\n    \"1 cup chocolate chips\"\n  ]\n}"
          }
        ],
        "role": "model"
      }
    }
  ]
}

之後 (Interactions API)

在 Interactions API 中，輸出格式控制項會移至頂層 response_format 陣列。

Python

from google import genai
from pydantic import BaseModel

client = genai.Client()

class Recipe(BaseModel):
    recipe_name: str
    ingredients: list[str]

interaction = client.interactions.create(
    model="gemini-3.6-flash",
    input="Give me a recipe for chocolate chip cookies.",
    response_format=[
        {
            "type": "text",
            "mime_type": "application/json",
            "schema": Recipe.model_json_schema(),
        }
    ],
)

print(interaction.output_text)

JavaScript

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

const client = new GoogleGenAI({});

const interaction = await client.interactions

遷移至 Interactions API

為何要遷移？

基本輸入/輸出

之前 (generateContent)

Python

JavaScript

REST

Python

JavaScript

REST

多轉折對話

之前 (generateContent)

Python

JavaScript

REST

之後 (Interactions API)

Python

JavaScript

REST

多模態輸入內容

之前 (generateContent)

Python

JavaScript

REST

之後 (Interactions API)

Python

JavaScript

REST

結構化輸出內容

之前 (generateContent)

Python

JavaScript

REST

之後 (Interactions API)

Python

JavaScript

之前 (`generateContent`)

之前 (`generateContent`)

之前 (`generateContent`)

之前 (`generateContent`)