流式互动

创建互动时，您可以将 stream: true 设置为使用 服务器发送事件 (SSE) 增量式流式传输响应。

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]

事件类型

每个服务器发送事件都包含一个名为 event_type 的名称和关联的 JSON 数据。Interactions API 使用对称流式传输模型，其中所有内容（文本、工具调用、思考）都通过一致的基于步骤 的事件流动。

每个流都遵循以下事件流：

  1. interaction.created：互动已创建，包含元数据（ID、模型、状态）。
  2. 一系列步骤，每个步骤都包含：
    • step.start 事件，指示步骤类型（例如 model_output、thought、function_call）。
    • 一个或多个 step.delta 事件，其中包含该步骤的增量数据。
    • step.stop 事件，将该步骤标记为已完成。
  3. interaction.completed 事件，其中包含最终的 usage 统计信息。

如果您将 stream: false 设置为 ，API 会返回一个包含 steps 数组的 interaction 对象。steps 中的每个元素都是一个 step.start → step.delta(s) → step.stop 周期的完整组装版本。

interaction.created

在首次创建互动时发送。包含互动 ID、模型和初始状态。

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

interaction.status_update

表示互动级状态转换。可能会出现在步骤之间。

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

step.start

标记新步骤的开始。包含步骤 type 和 index。步骤类型决定了要预期的增量类型以及步骤在非流式传输响应中的显示方式：

步骤类型 预期增量类型 说明
model_output text、image、audio 模型的最终响应内容。
thought thought_signature、thought_summary 思维链推理。仅当启用 thinking_summaries 时，才会显示 summary。
function_call arguments_delta 客户端执行函数的请求。将互动状态设置为 requires_action。
服务器端工具 因工具而异 由 API 执行的工具（例如 google_search_call、google_search_result、code_execution_call、code_execution_result）。

如需查看完整列表，请参阅 Interactions API 参考文档。

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

对于函数调用，该步骤包含函数名称、ID 和空实参 {}

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

step.delta

当前步骤的增量数据。delta 对象包含一个 type 字段，用于确定其形状。

示例：

text： 来自 model_output 步骤的增量文本令牌：

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： 来自 model_output 步骤的 Base64 编码图片数据：

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

thought_summary: 来自 thought 步骤的思考摘要内容：

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： 函数调用实参的（部分）JSON 字符串。必须跨增量累积：

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

这些是一些最常见的增量类型。如需查看所有增量类型的完整列表，请参阅 Interactions API 参考文档。

step.stop

标记步骤的结束。包含步骤 index。

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

interaction.completed

在互动完成时发送。包含带有 usage 统计信息的最终互动对象。在非流式传输模式下，这是顶级响应对象本身。不包含响应中的 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

在互动期间发生错误时发送。包含一个错误对象，其中包含消息和代码。

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

使用工具进行流式传输

Interactions API 支持在单个请求中使用客户端工具（函数调用）和服务器端工具（Google 搜索、代码执行等）进行流式传输。在流式传输期间，工具调用在事件流中显示为类型化步骤。对于函数调用，step.start 事件会传递函数名称，而 step.delta 事件会将实参作为 JSON 字符串 (arguments_delta) 进行流式传输。您必须累积这些增量才能获得完整的实参。 Google 搜索等服务器端工具由 API 自动执行，生成 google_search_call 和 google_search_result 步骤。

使用函数调用进行流式传输

如需使用流式传输执行函数调用，客户端必须处理多轮对话：

  1. 第 1 轮（函数请求）： 使用 stream: true 和您定义的 tools 调用 interactions.create。API 将流式传输 function_call 步骤。您必须累积 step.delta 事件中的增量实参 JSON 字符串 (arguments_delta)，直到互动以状态 requires_action 完成。
  2. 第 2 轮（发送结果）： 再次调用 interactions.create，传递 previous_interaction_id（与第一个互动的 ID 匹配），并在 input 数组中发送 function_result 块。这会恢复流，使模型能够生成最终响应。

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 轮： 请求函数调用

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 轮： 使用第 1 轮中的 previous_interaction_id 和 call_id 发送函数结果

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\"}"
            }
          ]
        }
      }
    ]
  }'

使用多个工具进行流式传输

以下示例在一个请求中同时使用了 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]

使用思考进行流式传输

当模型使用思考时，您将收到 thought 步骤，其中包含两种不同的增量类型：thought_summary（增量文本或图片摘要内容）和 thought_signature（模型内部推理的加密表示形式，在 step.stop 之前作为最后一个增量发送）。如果启用了 thinking_summaries，thought_summary 增量会流式传输模型推理的摘要。如需详细了解思考，请参阅思考指南。

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"}

...

使用智能体进行流式传输

Interactions API 支持 Deep Research 等智能体。智能体使用 background=True 并异步返回结果，但您也可以流式传输智能体互动，以便在互动发生时接收进度更新和中间步骤。如需了解详情，请参阅 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]

流式传输图片生成

Interactions API 支持同时流式传输多种输出模态。通过在 response_format 中同时请求 text 和 image，您可以在同一流中接收交错的文本和生成的图片。

以下示例使用 gemini-3.1-flash-image-preview (Nano Banana 2) 搜索信息并生成带有交错插图的故事。

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]

处理未知事件

根据 API 的版本控制政策，随着时间的推移，可能会添加新的事件类型和增量类型。您的代码应妥善处理未知事件类型，记录并跳过您无法识别的任何事件，而不是抛出错误。

后续步骤