串流互動

建立 Interaction 時，您可以設定 stream: true，使用伺服器傳送事件 (SSE) 逐步串流回應。

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Count to from 1 to 25.",
    stream=True,
)
for event in stream:
    if event.event_type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)

JavaScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Count to from 1 to 25.",
    stream: true,
});
for await (const event of stream) {
    if (event.event_type === "step.delta") {
        if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        }
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": "Count to from 1 to 25.",
    "stream": true
  }'

event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3-flash-preview"},"event_type":"interaction.created"}

event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}

event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}

event: step.stop
data: {"index":0,"event_type":"step.stop"}

event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}

event: step.delta
data: {"index":1,"delta":{"text":"1, 2, 3, 4, 5, 6, ","type":"text"},"event_type":"step.delta"}

event: step.delta
data: {"index":1,"delta":{"text":"7, 8, 9, 10, 11, 12, 13,","type":"text"},"event_type":"step.delta"}

...

event: step.stop
data: {"index":1,"event_type":"step.stop"}

event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":346,"total_input_tokens":11,"input_tokens_by_modality":[{"modality":"text","tokens":11}],"total_cached_tokens":0,"total_output_tokens":90,"total_tool_use_tokens":0,"total_thought_tokens":245},"created":"2026-05-12T18:44:51Z","updated":"2026-05-12T18:44:51Z","service_tier":"standard","object":"interaction","model":"gemini-3-flash-preview"},"event_type":"interaction.completed"}

event: done
data: [DONE]

事件類型

每個伺服器傳送的事件都包含具名的 event_type 和相關聯的 JSON 資料。Interactions API 採用對稱串流模型，所有內容 (文字、工具呼叫、思考) 都會透過一致的步驟式事件流動。

每個串流都會遵循下列事件流程：

  1. interaction.created：互動已建立，包含中繼資料 (ID、模型、狀態)。
  2. 一系列的步驟，每個步驟都包含：
    • step.start 事件，表示步驟類型 (例如 model_output、thought、function_call)。
    • 一或多個 step.delta 事件，其中包含該步驟的增量資料。
    • step.stop 事件，將步驟標示為完成。
  3. 含有最終 usage 統計資料的 interaction.completed 事件。

設定 stream: false 時，API 會傳回含有 steps 陣列的單一 interaction 物件。steps 中的每個元素都是一個 step.start → step.delta(s) → step.stop 週期完整組裝的版本。

interaction.created

互動首次建立時傳送。包含互動 ID、模型和初始狀態。

event: interaction.created
data: {"interaction": {"id": "...", "model": "gemini-3-flash-preview", "status": "in_progress", "object": "interaction"}, "event_type": "interaction.created"}

interaction.status_update

發出互動層級狀態轉換的信號。可能出現在步驟之間。

event: interaction.status_update
data: {"interaction_id": "...", "status": "in_progress", "event_type": "interaction.status_update"}

step.start

標示新步驟的開頭。包含步驟 type 和 index。步驟類型會決定預期出現的 delta 類型，以及步驟在非串流回應中的顯示方式：

步驟類型 預期差異遷移類型 說明
model_output text、image、audio 模型的最終回覆內容。
thought thought_signature、thought_summary 思維鏈推論。只有在啟用 thinking_summaries 時，才會顯示 summary。
function_call arguments_delta 要求用戶端執行函式。將互動狀態設為 requires_action。
伺服器端工具 依工具而異 API 執行的工具 (例如 google_search_call、google_search_result、code_execution_call、code_execution_result)。

如需完整清單，請參閱 Interactions API 參考資料。

event: step.start
data: {"index": 0, "step": {"type": "model_output"}, "event_type": "step.start"}

如果是函式呼叫，步驟會包含函式名稱、ID 和空白引數 {}

event: step.start
data: {"index": 0, "step": {"type": "function_call", "id":"un6k8t18", "name": "get_weather", "arguments":{}}, "event_type": "step.start"}

step.delta

目前步驟的增量資料。delta 物件包含 type 欄位，可決定形狀。

範例：

text：model_output 步驟的增量文字符記：

event: step.delta
data: {"index": 0, "delta": {"type": "text", "text": "Hello, my name is Phil"}, "event_type": "step.delta"}

event: step.delta
data: {"index": 0, "delta": {"type": "text", "text": ", and I live in Germany." }, "event_type": "step.delta"}

image：來自 model_output 步驟的 Base64 編碼圖片資料：

event: step.delta
data: {"index": 0, "delta": {"type": "image", "mime_type": "image/jpeg", "data": "/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoHBwgHBgoICAgLCg..."}, "event_type": "step.delta"}

thought_summary：從 thought 步驟取得的思考摘要內容：

event: step.delta
data: {"index": 0, "delta": {"type": "thought_summary", "content": {"type": "text", "text": "I need to find the GCD..."}}, "event_type": "step.delta"}

arguments_delta： (部分) 函式呼叫引數的 JSON 字串。必須跨 Delta 累計：

event: step.delta
data: {"index": 0, "delta": {"type": "arguments_delta", "arguments": "{\"location\": \"San Francisco, CA\"}"}, "event_type": "step.delta"}

以下列舉一些最常見的差異類型。如要查看所有差異類型的完整清單，請參閱 Interactions API 參考資料。

step.stop

標示步驟的結尾。包含步驟 index。

event: step.stop
data: {"index": 0, "event_type": "step.stop"}

interaction.completed

互動結束時傳送。包含最終互動物件和 usage 統計資料。在非串流模式下，這是頂層的回應物件本身。回覆內容不包含 steps。

event: interaction.completed
data: {"interaction": {"id": "v1_abc123", "status": "completed", "usage": {"total_input_tokens": 7, "total_output_tokens": 12, "total_tokens": 19}}, "event_type": "interaction.completed"}

error

互動期間發生錯誤時傳送。包含含有訊息和代碼的錯誤物件。

event: error
data: {"error":{"message":"Deadline expired before operation could complete.","code":"gateway_timeout"},"event_type":"error"}

使用工具串流播放

在單一要求中，Interactions API 支援使用用戶端工具 (函式呼叫) 和伺服器端工具 (Google 搜尋、程式碼執行等) 進行串流。在串流期間，工具呼叫會以輸入步驟的形式顯示在事件串流中。如果是函式呼叫，step.start 事件會傳送函式名稱，而 step.delta 事件會將引數串流為 JSON 字串 (arguments_delta)。您必須累計這些差異，才能取得完整引數。API 會自動執行 Google 搜尋等伺服器端工具，產生 google_search_call 和 google_search_result 步驟。

使用函式呼叫串流

如要使用串流功能呼叫函式，用戶端必須處理多輪對話： 1. 第 1 輪 (函式要求)：使用 stream: true 和您定義的 tools 呼叫 interactions.create。API 會串流 function_call 步驟。您必須從 step.delta 事件累計增量引數 JSON 字串 (arguments_delta)，直到互動以 requires_action 狀態完成為止。 2. 第 2 輪 (傳送結果)：再次呼叫 interactions.create，傳遞 previous_interaction_id (與第一次互動的 ID 相符)，並在 input 陣列中傳送 function_result 區塊。這會繼續串流，讓模型生成最終回覆。

Python

from google import genai

client = genai.Client()

weather_tool = {
    "type": "function",
    "name": "get_weather",
    "description": "Get the current weather in a given location",
    "parameters": {
        "type": "object",
        "properties": {
            "location": {
                "type": "string",
                "description": "The city and state, e.g. San Francisco, CA"
            }
        },
        "required": ["location"]
    }
}

# Turn 1: Request function call
stream = client.interactions.create(
    model="gemini-3-flash-preview",
    tools=[weather_tool],
    input="What is the weather in Paris right now?",
    stream=True,
)

first_interaction_id = None
func_call_id = None
func_call_name = None
func_args_accumulated = ""

for event in stream:
    if event.event_type == "interaction.created":
        first_interaction_id = event.interaction.id
    elif event.event_type == "step.start":
        step = event.step
        if step.type == "function_call":
            func_call_id = step.id
            func_call_name = step.name
    elif event.event_type == "step.delta":
        if event.delta.type == "arguments_delta":
            func_args_accumulated += event.delta.arguments

# Turn 2: Execute tool and send the result back to resume stream
if func_call_id:
    # Execute weather_tool using accumulated arguments
    # args = json.loads(func_args_accumulated)
    dummy_result = {
        "content": [{"type": "text", "text": '{"weather": "Sunny and 22°C"}'}]
    }

    stream2 = client.interactions.create(
        model="gemini-3-flash-preview",
        previous_interaction_id=first_interaction_id,
        input=[{
            "type": "function_result",
            "name": func_call_name,
            "call_id": func_call_id,
            "result": dummy_result
        }],
        stream=True,
    )

    for event in stream2:
        if event.event_type == "step.delta":
            if event.delta.type == "text":
                print(event.delta.text, end="", flush=True)

JavaScript

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

const client = new GoogleGenAI({});

const weatherTool = {
    type: "function",
    name: "get_weather",
    description: "Get the current weather in a given location",
    parameters: {
        type: "object",
        properties: {
            location: {
                type: "string",
                description: "The city and state, e.g. San Francisco, CA"
            }
        },
        required: ["location"]
    }
};

// Turn 1: Request function call
const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    tools: [weatherTool],
    input: "What is the weather in Paris right now?",
    stream: true,
});

let firstInteractionId = null;
let funcCallId = null;
let funcCallName = null;
let funcArgsAccumulated = "";

for await (const event of stream) {
    if (event.event_type === "interaction.created") {
        firstInteractionId = event.interaction.id;
    } else if (event.event_type === "step.start") {
        const step = event.step;
        if (step.type === "function_call") {
            funcCallId = step.id;
            funcCallName = step.name;
        }
    } else if (event.event_type === "step.delta") {
        if (event.delta.type === "arguments_delta") {
            funcArgsAccumulated += event.delta.arguments;
        }
    }
}

// Turn 2: Execute tool and send the result back to resume stream
if (funcCallId && firstInteractionId && funcCallName) {
    // const args = JSON.parse(funcArgsAccumulated);
    const dummyResult = {
        content: [{ type: "text", text: '{"weather": "Sunny and 22°C"}' }]
    };

    const stream2 = await client.interactions.create({
        model: "gemini-3-flash-preview",
        previous_interaction_id: firstInteractionId,
        input: [{
            type: "function_result",
            name: funcCallName,
            call_id: funcCallId,
            result: dummyResult
        }],
        stream: true,
    });

    for await (const event of stream2) {
        if (event.event_type === "step.delta") {
            if (event.delta.type === "text") {
                process.stdout.write(event.delta.text);
            }
        }
    }
}

REST

第 1 輪：要求函式呼叫

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": "What is the weather in Paris right now?",
    "stream": true,
    "tools": [
      {
        "type": "function",
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            }
          },
          "required": ["location"]
        }
      }
    ]
  }'

第 2 輪：使用第 1 輪的 previous_interaction_id 和 call_id 傳送函式結果

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3-flash-preview",
    "previous_interaction_id": "v1_ChdGUVFJYXBXVUdLVEF4TjhQ...",
    "stream": true,
    "input": [
      {
        "type": "function_result",
        "name": "get_weather",
        "call_id": "CALL_ID",
        "result": {
          "content": [
            {
              "type": "text",
              "text": "{\"weather\": \"Sunny and 22°C\"}"
            }
          ]
        }
      }
    ]
  }'

使用多種工具直播

以下範例在一個要求中同時使用 function 工具和 google_search：

Python

from google import genai

client = genai.Client()

tools = [
    {"type": "google_search"},
    {
        "type": "function",
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
            "type": "object",
            "properties": {
                "location": {
                    "type": "string",
                    "description": "The city and state, e.g. San Francisco, CA"
                }
            },
            "required": ["location"]
        }
    }
]

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    tools=tools,
    input="Search what it the largest mountain in Europe and what the weather is there right now?",
    stream=True,
)
for event in stream:
    if event.event_type == "step.start":
        step = event.step
        print(f"\n--- Step {event.index}: {step.type} ---")
        # Show details for tool steps
        if step.type == "google_search_call":
            print(f"  Search ID: {step.id}")
        elif step.type == "google_search_result":
            print(f"  Result for: {step.call_id}")
        elif step.type == "function_call":
            print(f"  Function: {step.name}({step.arguments})")
    elif event.event_type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)
        elif event.delta.type == "google_search_call":
            print(f"  Queries: {event.delta.arguments}")
        elif event.delta.type == "arguments_delta":
            print(f"  Args chunk: {event.delta.arguments}", end="", flush=True)
    elif event.event_type == "interaction.completed":
        print(f"\n\nStatus: {event.interaction.status}")
        if event.interaction.status == "requires_action":
            print("Action required: provide function call results to continue.")

JavaScript

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

const client = new GoogleGenAI({});

const tools = [
    { type: "google_search" },
    {
        type: "function",
        name: "get_weather",
        description: "Get the current weather in a given location",
        parameters: {
            type: "object",
            properties: {
                location: {
                    type: "string",
                    description: "The city and state, e.g. San Francisco, CA"
                }
            },
            required: ["location"]
        }
    }
];

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    tools: tools,
    input: "Search what it the largest mountain in Europe and what the weather is there right now?",
    stream: true,
});
for await (const event of stream) {
    if (event.event_type === "step.start") {
        const step = event.step;
        console.log(`\n--- Step ${event.index}: ${step.type} ---`);
        // Show details for tool steps
        if (step.type === "google_search_call") {
            console.log(`  Search ID: ${step.id}`);
        } else if (step.type === "google_search_result") {
            console.log(`  Result for: ${step.call_id}`);
        } else if (step.type === "function_call") {
            console.log(`  Function: ${step.name}(${JSON.stringify(step.arguments)})`);
        }
    } else if (event.event_type === "step.delta") {
        if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        } else if (event.delta.type === "google_search_call") {
            console.log(`  Queries: ${JSON.stringify(event.delta.arguments?.queries)}`);
        } else if (event.step.type === "google_search_result") {
            console.log(`  Result for: ${event.step.call_id}`);
        } else if (event.delta.type === "arguments_delta") {
            process.stdout.write(`  Args chunk: ${event.delta.arguments}`);
        }
    } else if (event.event_type === "interaction.completed") {
        console.log(`\n\nStatus: ${event.interaction.status}`);
        if (event.interaction.status === "requires_action") {
            console.log("Action required: provide function call results to continue.");
        }
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": "Search what it the largest mountain in Europe and what the weather is there right now?",
    "stream": true,
    "tools": [
      { "type": "google_search" },
      {
        "type": "function",
        "name": "get_weather",
        "description": "Get the current weather in a given location",
        "parameters": {
          "type": "object",
          "properties": {
            "location": {
              "type": "string",
              "description": "The city and state, e.g. San Francisco, CA"
            }
          },
          "required": ["location"]
        }
      }
    ]
  }'

event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3-flash-preview"},"event_type":"interaction.created"}

event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}

event: step.start
data: {"index":0,"step":{"id":"mkutnkgn","signature":"","type":"google_search_call"},"event_type":"step.start"}

event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"google_search_call","arguments":{"queries":["largest mountain in Europe"]}},"event_type":"step.delta"}

event: step.stop
data: {"index":0,"event_type":"step.stop"}

event: step.start
data: {"index":1,"step":{"call_id":"mkutnkgn","signature":"","type":"google_search_result"},"event_type":"step.start"}

event: step.delta
data: {"index":1,"delta":{"signature":"...","type":"google_search_result","is_error":false},"event_type":"step.delta"}

event: step.stop
data: {"index":1,"event_type":"step.stop"}

event: step.start
data: {"index":2,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":2,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}

event: step.stop
data: {"index":2,"event_type":"step.stop"}

event: step.start
data: {"index":3,"step":{"id":"ktr5aysg","type":"function_call","name":"get_weather","arguments":{}},"event_type":"step.start"}

event: step.delta
data: {"index":3,"delta":{"arguments":"{\"location\":\"Mount Elbrus, Russia\"}","type":"arguments_delta"},"event_type":"step.delta"}

event: step.stop
data: {"index":3,"event_type":"step.stop"}

event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"requires_action","usage":{"total_tokens":299,"total_input_tokens":138,"input_tokens_by_modality":[{"modality":"text","tokens":138}],"total_cached_tokens":0,"total_output_tokens":20,"total_tool_use_tokens":0,"total_thought_tokens":141},"created":"2026-05-12T17:24:26Z","updated":"2026-05-12T17:24:26Z","service_tier":"standard","object":"interaction","model":"gemini-3-flash-preview"},"event_type":"interaction.completed"}

event: done
data: [DONE]

串流播放並顯示思考過程

模型進行思考時，您會收到 thought 步驟，其中包含兩種不同的 delta 類型：thought_summary (遞增文字或圖像摘要內容) 和 thought_signature (模型內部推論的加密表示法，會在 step.stop 前傳送為最後一個 delta)。如果啟用 thinking_summaries，thought_summary delta 會串流傳輸模型推論的摘要。如要進一步瞭解思考，請參閱思考指南。

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3-flash-preview",
    input="What is the greatest common divisor of 1071 and 462?",
    generation_config={
        "thinking_summaries": "auto"
    },
    stream=True,
)
for event in stream:
    if event.event_type == "step.start":
        print(f"\n--- Step: {event.step.type} ---")
    elif event.event_type == "step.delta":
        if event.delta.type == "thought_summary":
            if event.delta.content.type == "text":
                print(event.delta.content.text, end="", flush=True)
        elif event.delta.type == "text":
            print(event.delta.text, end="", flush=True)

JavaScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is the greatest common divisor of 1071 and 462?",
    generation_config: {
        thinking_summaries: "auto",
    },
    stream: true,
});
for await (const event of stream) {
    if (event.event_type === "step.start") {
        console.log(`\n--- Step: ${event.step.type} ---`);
    } else if (event.event_type === "step.delta") {
        if (event.delta.type === "thought_summary") {
            if (event.delta.content.type === "text") {
                process.stdout.write(event.delta.content.text);
            }
        } else if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        }
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3-flash-preview",
    "input": "What is the greatest common divisor of 1071 and 462?",
    "stream": true,
    "generation_config": {
      "thinking_summaries": "auto"
    }
  }'

event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3-flash-preview"},"event_type":"interaction.created"}

event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}

event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":0,"delta":{"content":{"text":"**Implementing Euclidean Algorithm**\n\nI've just worked through a detailed example applying the Euclidean algorithm to find the GCD of 1071 and 462, confirming its step-by-step nature. The calculations went smoothly, tracking the remainders until zero. My focus is now solidifying the implementation logic, ensuring accuracy and considering potential edge cases. I'll translate this example into code.\n\n\n","type":"text"},"type":"thought_summary"},"event_type":"step.delta"}

event: step.delta
data: {"index":0,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}

event: step.stop
data: {"index":0,"event_type":"step.stop"}

event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}

...

使用代理串流播放

Interactions API 支援 Deep Research 等代理程式。代理程式會使用 background=True 並以非同步方式傳回結果，但您也可以串流代理程式互動，即時接收進度更新和中間步驟。詳情請參閱 Deep Research 指南。

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    agent="deep-research-preview-04-2026",
    input="Research the latest advances in quantum computing.",
    stream=True,
    background=True,
    agent_config={
        "type": "deep-research",
        "thinking_summaries": "auto"
    }
)
for event in stream:
    if event.event_type == "step.start":
        print(f"\n--- Step: {event.step.type} ---")
    elif event.event_type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)
        elif event.delta.type == "thought_summary":
            if event.delta.content.type == "text":
                print(event.delta.content.text, end="", flush=True)
    elif event.event_type == "interaction.completed":
        print(f"\n\nTotal Tokens: {event.interaction.usage.total_tokens}")

JavaScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    agent: "deep-research-preview-04-2026",
    input: "Research the latest advances in quantum computing.",
    stream: true,
    background: true,
    agent_config: {
        type: "deep-research",
        thinking_summaries: "auto"
    }
});
for await (const event of stream) {
    if (event.event_type === "step.start") {
        console.log(`\n--- Step: ${event.step.type} ---`);
    } else if (event.event_type === "step.delta") {
        if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        } else if (event.delta.type === "thought_summary") {
            if (event.delta.content.type === "text") {
                process.stdout.write(event.delta.content.text);
            }
        }
    } else if (event.event_type === "interaction.completed") {
        console.log(`\n\nTotal Tokens: ${event.interaction.usage.total_tokens}`);
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "agent": "deep-research-preview-04-2026",
    "input": "Research the latest advances in quantum computing.",
    "stream": true,
    "background": true,
    "agent_config": {
      "type": "deep-research",
      "thinking_summaries": "auto"
    }
  }'

event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","agent":"deep-research-preview-04-2026"},"event_type":"interaction.created"}

event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}

event: step.start
data: {"index":0,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":0,"delta":{"content":{"text":"***Generating research plan***\n\nTo best answer your request, I'm starting by constructing a comprehensive research plan. This will outline the key areas I need to investigate and the strategy I'll use to connect them."},"type":"thought_summary"},"event_type":"step.delta"}

... (additional thought steps) ...

event: step.stop
data: {"index":0,"event_type":"step.stop"}

event: step.start
data: {"index":1,"step":{"type":"model_output"},"event_type":"step.start"}

event: step.delta
data: {"index":1,"delta":{"text":"# The Quantum Inflection Point: Exhaustive Analysis of Hardware, Algorithms, and Market Dynamics in 2026\n\n## Executive Summary\n\n..."},"event_type":"step.delta"}

event: step.stop
data: {"index":1,"event_type":"step.stop"}

event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":1117031,"total_input_tokens":428865,"total_output_tokens":22294,"total_thought_tokens":26213},"created":"2026-05-12T17:24:27Z","updated":"2026-05-12T17:24:27Z","object":"interaction","agent":"deep-research-preview-04-2026"},"event_type":"interaction.completed"}

event: done
data: [DONE]

串流生成圖像

Interactions API 支援同時串流多種輸出模態。在 response_format 中同時要求 text 和 image，即可在同一串流中收到交錯的文字和生成的圖片。

以下範例使用 gemini-3.1-flash-image-preview (Nano Banana 2) 搜尋資訊，並生成穿插插圖的故事。

Python

from google import genai

client = genai.Client()

stream = client.interactions.create(
    model="gemini-3.1-flash-image-preview",
    tools=[{"type": "google_search", "search_types": ["web_search", "image_search"]}],
    input="Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
    response_format=[
        {"type": "text"},
        {"type": "image"}
    ],
    stream=True,
)

for event in stream:
    if event.event_type == "step.delta":
        if event.delta.type == "text":
            print(event.delta.text, end="", flush=True)
        elif event.delta.type == "image":
            print(f"\n[Image chunk: {len(event.delta.data)} bytes]", end="", flush=True)

JavaScript

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

const client = new GoogleGenAI({});

const stream = await client.interactions.create({
    model: "gemini-3.1-flash-image-preview",
    tools: [{ type: "google_search", search_types: ["web_search", "image_search"] }],
    input: "Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
    response_format: [
        { type: "text" },
        { type: "image" }
    ],
    stream: true,
});

for await (const event of stream) {
    if (event.event_type === "step.delta") {
        if (event.delta.type === "text") {
            process.stdout.write(event.delta.text);
        } else if (event.delta.type === "image") {
            console.log(`\n[Image chunk: ${event.delta.data.length} bytes]`);
        }
    }
}

REST

curl -X POST "https://generativelanguage.googleapis.com/v1beta/interactions" \
  -H "x-goog-api-key: $GEMINI_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Api-Revision: 2026-05-20" \
  --no-buffer \
  -d '{
    "model": "gemini-3.1-flash-image-preview",
    "input": "Search for the history of the Colosseum and write a short illustrated story about a gladiator named Marcus. Interleave text and generated images.",
    "stream": true,
    "tools": [
      { "type": "google_search",
        "search_types": ["web_search", "image_search"]
      }
    ],
    "generation_config": {
      "thinking_summaries": "auto"
    },
    "response_format": [
      { "type": "text" }, { "type": "image"}
    ]
  }'

event: interaction.created
data: {"interaction":{"id":"v1_...","status":"in_progress","object":"interaction","model":"gemini-3.1-flash-image-preview"},"event_type":"interaction.created"}

event: interaction.status_update
data: {"interaction_id":"v1_...","status":"in_progress","event_type":"interaction.status_update"}

event: step.start
data: {"index":0,"step":{"type":"model_output"},"event_type":"step.start"}

event: step.delta
data: {"index":0,"delta":{"text":"Here is a short illustrated story about the Colosseum...\n\n### Part 1: The New Flavian Amphitheater\n\n...","type":"text"},"event_type":"step.delta"}

...

event: step.stop
data: {"index":0,"event_type":"step.stop"}

event: step.start
data: {"index":1,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":1,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}

event: step.stop
data: {"index":1,"event_type":"step.stop"}

event: step.start
data: {"index":2,"step":{"type":"model_output"},"event_type":"step.start"}

event: step.delta
data: {"index":2,"delta":{"mime_type":"image/jpeg","data":"/9j/4AAQSkZJRgABAQAAAQABAAD/2wBDAAoHBwgHBgoICAgLCg...","type":"image"},"event_type":"step.delta"}

event: step.delta
data: {"index":2,"delta":{"text":"### Part 2: The Hypogeum and the Wait\n\n...","type":"text"},"event_type":"step.delta"}

...

event: step.stop
data: {"index":2,"event_type":"step.stop"}

event: step.start
data: {"index":3,"step":{"type":"thought"},"event_type":"step.start"}

event: step.delta
data: {"index":3,"delta":{"signature":"...","type":"thought_signature"},"event_type":"step.delta"}

event: step.stop
data: {"index":3,"event_type":"step.stop"}

event: step.start
data: {"index":4,"step":{"type":"model_output"},"event_type":"step.start"}

event: step.delta
data: {"index":4,"delta":{"mime_type":"image/jpeg","data":"/9j/4AAQSkZJRgABAQAAAQABAAD/...","type":"image"},"event_type":"step.delta"}

event: step.delta
data: {"index":4,"delta":{"text":"### Part 3: The Moment of Spectacle\n\n...","type":"text"},"event_type":"step.delta"}

...

event: step.stop
data: {"index":4,"event_type":"step.stop"}

event: interaction.completed
data: {"interaction":{"id":"v1_...","status":"completed","usage":{"total_tokens":6128,"total_input_tokens":29,"total_output_tokens":6099,"output_tokens_by_modality":[{"modality":"image","tokens":4480}]}},"event_type":"interaction.completed"}

event: done
data: [DONE]

處理不明事件

根據 API 的版本管理政策，系統可能會隨時間新增事件類型和差異類型。程式碼應妥善處理不明事件類型，記錄並略過無法辨識的事件，而非擲回錯誤。

後續步驟