gemini-interactions-api

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Gemini Interactions API Skill

Gemini Interactions API 技能指南

This skill provides instructions for authenticating, connecting to, and utilizing the stateful, server-managed Gemini Interactions API on Gemini Enterprise Agent Platform.

The Interactions API is the modern, recommended way to execute Generative AI agent conversations, background research tasks, multi-turn chats, and structured, multi-step workflows.

[!IMPORTANT] CRITICAL: Unified SDK & Latest Models
Unified SDK: Use the Google Gen AI SDK (google-genai >= 2.0.0
for Python, @google/genai >= 2.0.0
for JS/TS). Legacy SDKs like
google-cloud-aiplatform
,
@google-cloud/vertexai
, and
google-generativeai
are strictly unsupported for Interactions.
Latest Models Only: Use
gemini-3.1-pro-preview
,
gemini-3.1-flash-lite
,
gemini-3-flash-preview
,
gemini-2.5-pro
, or
gemini-2.5-flash
. Refer to the latest model versions to check for new updates. Legacy models (
gemini-2.0-*
,
gemini-1.5-*
) are deprecated and do not support interactions.
Turn-Scoped Parameters: Parameters like
tools
,
system_instruction
, and
generation_config
are turn-scoped. They MUST be passed with each interaction request.

本技能文档提供了在Gemini Enterprise Agent Platform上进行认证、连接以及使用有状态、服务器托管的Gemini Interactions API的操作说明。

Interactions API是执行生成式AI代理对话、后台研究任务、多轮聊天以及结构化多步骤工作流的现代化推荐方案。

[!IMPORTANT] 重要提示：统一SDK与最新模型
统一SDK：请使用Google Gen AI SDK（Python版本需**
google-genai >= 2.0.0
，JS/TS版本需
@google/genai >= 2.0.0
**）。旧版SDK如
google-cloud-aiplatform
、
@google-cloud/vertexai
和
google-generativeai
完全不支持Interactions功能。
仅使用最新模型：请使用
gemini-3.1-pro-preview
、
gemini-3.1-flash-lite
、
gemini-3-flash-preview
、
gemini-2.5-pro
或
gemini-2.5-flash
。可参考最新模型版本查看更新信息。旧版模型（
gemini-2.0-*
、
gemini-1.5-*
）已被弃用，不支持Interactions功能。
轮次作用域参数：
tools
、
system_instruction
和
generation_config
等参数属于轮次作用域，必须随每次交互请求一并传递。

1. Authentication

1. 身份认证

Before running any code, ensure you are authenticated with Application Default Credentials (ADC) and have the necessary API enabled.

Login:
bash
```
gcloud auth application-default login
```

Enable API (if not already enabled):

bash

gcloud services enable aiplatform.googleapis.com

在运行任何代码之前，请确保已通过Application Default Credentials（ADC）完成身份认证，并启用了所需的API。

启用API（若尚未启用）：

bash

gcloud services enable aiplatform.googleapis.com

2. Client Initialization

2. 客户端初始化

You can initialize the client using environment variables (recommended) or by passing explicit configuration parameters.

你可以使用环境变量（推荐方式）或传入显式配置参数来初始化客户端。

Option A: Environment Variables (Recommended)

选项A：环境变量（推荐）

Configure environment variables to let the SDK automatically resolve settings:

bash

export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"

配置环境变量，让SDK自动解析设置：

bash

export GOOGLE_GENAI_USE_ENTERPRISE=true
export GOOGLE_CLOUD_PROJECT="your-project-id"
export GOOGLE_CLOUD_LOCATION="global"

Python

python

from google import genai

python

from google import genai

The SDK automatically picks up the environment variables

SDK会自动读取环境变量

client = genai.Client()

undefined

client = genai.Client()

undefined

TypeScript/JavaScript

typescript

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

// The SDK automatically picks up the environment variables
const ai = new GoogleGenAI();

typescript

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

// SDK会自动读取环境变量
const ai = new GoogleGenAI();

Option B: Explicit Inline Parameters

选项B：显式内联参数

Alternatively, pass configuration values directly inside your code:

或者，直接在代码中传入配置值：

Python

python

from google import genai
import google.auth

_, project_id = google.auth.default()
client = genai.Client(enterprise=True, project=project_id, location="global")

python

from google import genai
import google.auth

_, project_id = google.auth.default()
client = genai.Client(enterprise=True, project=project_id, location="global")

TypeScript/JavaScript

typescript

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

const ai = new GoogleGenAI({
    enterprise: {
        project: "your-project-id",
        location: "global"
    }
});

typescript

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

const ai = new GoogleGenAI({
    enterprise: {
        project: "your-project-id",
        location: "global"
    }
});

3. Core Interactions API Usage

3. Interactions API核心用法

Quick Start (Single-Turn)

快速入门（单轮对话）

Submit a single prompt and read the final text response. Under the modern schema, output content is retrieved from the

steps

list.

提交单个提示并读取最终文本响应。在现代架构下，输出内容从

steps

列表中获取。

Python

python

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain serverless computing in one sentence."
)

python

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Explain serverless computing in one sentence."
)

Output text is located under steps

输出文本位于steps下

print(interaction.steps[-1].content[0].text)

undefined

print(interaction.steps[-1].content[0].text)

undefined

TypeScript/JavaScript

typescript

const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain serverless computing in one sentence."
});
console.log(interaction.steps[interaction.steps.length - 1].content[0].text);

typescript

const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Explain serverless computing in one sentence."
});
console.log(interaction.steps[interaction.steps.length - 1].content[0].text);

Stateful Conversation (Multi-Turn)

有状态对话（多轮对话）

Interactions are stateful by default. Store the conversation state in the cloud and reference it in the subsequent turn using

previous_interaction_id

Interactions默认是有状态的。将对话状态存储在云端，并在后续轮次中使用

previous_interaction_id

引用该状态。

Python

python

undefined

python

undefined

Turn 1: Introduce ourselves

第一轮：自我介绍

turn1 = client.interactions.create( model="gemini-3-flash-preview", input="Hi! My name is John. I am working on AI agents.", store=True ) print(f"Turn 1: {turn1.steps[-1].content[0].text}")

Turn 2: Refer back to the stored turn state

第二轮：引用已存储的对话状态

turn2 = client.interactions.create( model="gemini-3-flash-preview", input="What is my name?", previous_interaction_id=turn1.id ) print(f"Turn 2: {turn2.steps[-1].content[0].text}")

undefined

turn2 = client.interactions.create( model="gemini-3-flash-preview", input="What is my name?", previous_interaction_id=turn1.id ) print(f"Turn 2: {turn2.steps[-1].content[0].text}")

undefined

TypeScript/JavaScript

typescript

// Turn 1
const turn1 = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Hi! My name is John. I am working on AI agents.",
    store: true
});

// Turn 2
const turn2 = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is my name?",
    previousInteractionId: turn1.id
});
console.log(turn2.steps[turn2.steps.length - 1].content[0].text);

typescript

// 第一轮
const turn1 = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Hi! My name is John. I am working on AI agents.",
    store: true
});

// 第二轮
const turn2 = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is my name?",
    previousInteractionId: turn1.id
});
console.log(turn2.steps[turn2.steps.length - 1].content[0].text);

Real-Time Streaming

实时流式传输

Stream responses in real-time. Passing

stream=True

returns an iterable chunk generator.

实时流式获取响应。传入

stream=True

会返回一个可迭代的分片生成器。

Python

python

response = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Write a short poem about debugging.",
    stream=True
)

for chunk in response:
    if chunk.steps:
        step = chunk.steps[-1]
        if step.content and step.content[0].text:
            print(step.content[0].text, end="", flush=True)
print()

python

response = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Write a short poem about debugging.",
    stream=True
)

for chunk in response:
    if chunk.steps:
        step = chunk.steps[-1]
        if step.content and step.content[0].text:
            print(step.content[0].text, end="", flush=True)
print()

TypeScript/JavaScript

typescript

const responseStream = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Write a short poem about debugging.",
    stream: true
});

for await (const chunk of responseStream) {
    if (chunk.steps) {
        const step = chunk.steps[chunk.steps.length - 1];
        if (step.content && step.content[0].text) {
            process.stdout.write(step.content[0].text);
        }
    }
}
console.log();

typescript

const responseStream = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Write a short poem about debugging.",
    stream: true
});

for await (const chunk of responseStream) {
    if (chunk.steps) {
        const step = chunk.steps[chunk.steps.length - 1];
        if (step.content && step.content[0].text) {
            process.stdout.write(step.content[0].text);
        }
    }
}
console.log();

Structured Output (Pydantic / Polymorphic

response_format

)

结构化输出（Pydantic / 多态

response_format

）

Retrieve structured, type-safe JSON matching a schema. Under the modern Interactions API, a polymorphic

response_format

argument directly takes the target schema structure.

获取与指定 schema 匹配的结构化、类型安全的JSON。在现代Interactions API中，多态

response_format

参数可直接传入目标schema结构。

Python

python

from pydantic import BaseModel, Field

class Book(BaseModel):
    title: str = Field(description="The title of the book")
    author: str = Field(description="The book's author")
    year_published: int

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Recommend one famous sci-fi book.",
    response_format=Book
)

python

from pydantic import BaseModel, Field

class Book(BaseModel):
    title: str = Field(description="The title of the book")
    author: str = Field(description="The book's author")
    year_published: int

interaction = client.interactions.create(
    model="gemini-3-flash-preview",
    input="Recommend one famous sci-fi book.",
    response_format=Book
)

The text will be a valid JSON matching the Book schema

返回的文本是符合Book schema的有效JSON

print(interaction.steps[-1].content[0].text)

undefined

print(interaction.steps[-1].content[0].text)

undefined

TypeScript/JavaScript

typescript

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

const BookSchema = {
    type: Type.OBJECT,
    properties: {
        title: { type: Type.STRING, description: "The title of the book" },
        author: { type: Type.STRING, description: "The book's author" },
        yearPublished: { type: Type.INTEGER }
    },
    required: ["title", "author", "yearPublished"]
};

const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Recommend one famous sci-fi book.",
    responseFormat: BookSchema
});

console.log(interaction.steps[interaction.steps.length - 1].content[0].text);

typescript

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

const BookSchema = {
    type: Type.OBJECT,
    properties: {
        title: { type: Type.STRING, description: "The title of the book" },
        author: { type: Type.STRING, description: "The book's author" },
        yearPublished: { type: Type.INTEGER }
    },
    required: ["title", "author", "yearPublished"]
};

const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "Recommend one famous sci-fi book.",
    responseFormat: BookSchema
});

console.log(interaction.steps[interaction.steps.length - 1].content[0].text);

Function Calling (Agent Tool Use)

函数调用（代理工具使用）

Define local tools (functions) and submit execution results to the stateful interaction history.

定义本地工具（函数）并将执行结果提交到有状态的交互历史中。

Python

python

def get_stock_price(ticker: str) -> float:
    """Gets the stock price for a given ticker symbol."""
    if ticker.upper() == "GOOG":
        return 175.50
    return 100.0

python

def get_stock_price(ticker: str) -> float:
    """Gets the stock price for a given ticker symbol."""
    if ticker.upper() == "GOOG":
        return 175.50
    return 100.0

Turn 1: Pass tools to the model

第一轮：向模型传递工具

interaction = client.interactions.create( model="gemini-3-flash-preview", input="What is the stock price of GOOG?", tools=[get_stock_price] )

last_step = interaction.steps[-1]

interaction = client.interactions.create( model="gemini-3-flash-preview", input="What is the stock price of GOOG?", tools=[get_stock_price] )

last_step = interaction.steps[-1]

Check if the model requested a function call

检查模型是否请求了函数调用

if last_step.tool_calls: for call in last_step.tool_calls: if call.name == "get_stock_price": ticker_arg = call.args.get("ticker") price = get_stock_price(ticker_arg)

        # Turn 2: Submit function execution result statefully
        final_turn = client.interactions.create(
            model="gemini-3-flash-preview",
            input=f"The stock price for {ticker_arg} is ${price}.",
            previous_interaction_id=interaction.id
        )
        print(final_turn.steps[-1].content[0].text)

undefined

if last_step.tool_calls: for call in last_step.tool_calls: if call.name == "get_stock_price": ticker_arg = call.args.get("ticker") price = get_stock_price(ticker_arg)

        # 第二轮：有状态地提交函数执行结果
        final_turn = client.interactions.create(
            model="gemini-3-flash-preview",
            input=f"The stock price for {ticker_arg} is ${price}.",
            previous_interaction_id=interaction.id
        )
        print(final_turn.steps[-1].content[0].text)

undefined

TypeScript/JavaScript

typescript

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

// Define local tool
function getStockPrice({ ticker }: { ticker: string }): number {
    if (ticker.toUpperCase() === "GOOG") {
        return 175.50;
    }
    return 100.00;
}

// Turn 1: Pass tools to the model
const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is the stock price of GOOG?",
    tools: [{
        functionDeclarations: [{
            name: "getStockPrice",
            description: "Gets the stock price for a given ticker symbol.",
            parameters: {
                type: Type.OBJECT,
                properties: {
                    ticker: { type: Type.STRING, description: "The stock ticker symbol" }
                },
                required: ["ticker"]
            }
        }]
    }]
});

const lastStep = interaction.steps[interaction.steps.length - 1];
// Check if the model requested a function call
if (lastStep.toolCalls) {
    for (const call of lastStep.toolCalls) {
        if (call.name === "getStockPrice") {
            const tickerArg = call.args.ticker as string;
            const price = getStockPrice({ ticker: tickerArg });

            // Turn 2: Submit function execution result statefully
            const finalTurn = await ai.interactions.create({
                model: "gemini-3-flash-preview",
                input: `The stock price for ${tickerArg} is $${price}.`,
                previousInteractionId: interaction.id
            });
            console.log(finalTurn.steps[finalTurn.steps.length - 1].content[0].text);
        }
    }
}

typescript

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

// 定义本地工具
function getStockPrice({ ticker }: { ticker: string }): number {
    if (ticker.toUpperCase() === "GOOG") {
        return 175.50;
    }
    return 100.00;
}

// 第一轮：向模型传递工具
const interaction = await ai.interactions.create({
    model: "gemini-3-flash-preview",
    input: "What is the stock price of GOOG?",
    tools: [{
        functionDeclarations: [{
            name: "getStockPrice",
            description: "Gets the stock price for a given ticker symbol.",
            parameters: {
                type: Type.OBJECT,
                properties: {
                    ticker: { type: Type.STRING, description: "The stock ticker symbol" }
                },
                required: ["ticker"]
            }
        }]
    }]
});

const lastStep = interaction.steps[interaction.steps.length - 1];
// 检查模型是否请求了函数调用
if (lastStep.toolCalls) {
    for (const call of lastStep.toolCalls) {
        if (call.name === "getStockPrice") {
            const tickerArg = call.args.ticker as string;
            const price = getStockPrice({ ticker: tickerArg });

            // 第二轮：有状态地提交函数执行结果
            const finalTurn = await ai.interactions.create({
                model: "gemini-3-flash-preview",
                input: `The stock price for ${tickerArg} is $${price}.`,
                previousInteractionId: interaction.id
            });
            console.log(finalTurn.steps[finalTurn.steps.length - 1].content[0].text);
        }
    }
}

4. Accessing the Interactions API via REST

4. 通过REST访问Interactions API

For shell-based scripts, debugging, or non-Python/JS environments, you can communicate with the stateful Interactions API directly using raw HTTP/REST requests via

curl

对于基于Shell的脚本、调试或非Python/JS环境，你可以通过

curl

直接使用原始HTTP/REST请求与有状态的Interactions API通信。

1. REST Endpoint

1. REST端点

The REST API endpoint for interactions is:

http

POST https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/interactions

LOCATION: Use
```
global
```
(or custom region if required).
PROJECT_ID: Your Google Cloud Project ID.

Interactions的REST API端点为：

http

POST https://aiplatform.googleapis.com/v1beta1/projects/{PROJECT_ID}/locations/{LOCATION}/interactions

LOCATION：使用
```
global
```
（如需自定义区域也可使用对应区域）。
PROJECT_ID：你的Google Cloud项目ID。

2. Set up Variables & Authentication Header

2. 设置变量与认证头

Set your target agent ID (e.g., model or custom agent path) and access token generated from Application Default Credentials:

bash

AGENT_ID="your-agent-id"
ACCESS_TOKEN=$(gcloud auth print-access-token)

设置目标代理ID（例如模型或自定义代理路径）以及从Application Default Credentials生成的访问令牌：

bash

AGENT_ID="your-agent-id"
ACCESS_TOKEN=$(gcloud auth print-access-token)

3. Single-Turn Interaction Payload

3. 单轮交互请求体

Send a request to start an interaction using the agent variable:

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Explain serverless computing in one sentence."
      }]
    }]
  }'

使用代理变量发送请求以启动交互：

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Explain serverless computing in one sentence."
      }]
    }]
  }'

Response Example

响应示例

A synchronous POST request returns a JSON object containing the conversation step details and unique identifiers:

json

{
  "id": "your-interaction-id",
  "status": "completed",
  "steps": [
    {
      "role": "model",
      "content": [
        {
          "type": "text",
          "text": "Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers, charging customers based on actual usage rather than pre-purchased capacity."
        }
      ]
    }
  ],
  "usage": {
    "total_tokens": 24751,
    "total_input_tokens": 23894,
    "total_output_tokens": 857
  },
  "created": "2026-05-08T10:44:43Z",
  "updated": "2026-05-08T10:44:43Z",
  "environment_id": "your-environment-id",
  "object": "interaction"
}

同步POST请求会返回包含对话步骤详情和唯一标识符的JSON对象：

json

{
  "id": "your-interaction-id",
  "status": "completed",
  "steps": [
    {
      "role": "model",
      "content": [
        {
          "type": "text",
          "text": "Serverless computing is a cloud execution model where the cloud provider dynamically manages the allocation and provisioning of servers, charging customers based on actual usage rather than pre-purchased capacity."
        }
      ]
    }
  ],
  "usage": {
    "total_tokens": 24751,
    "total_input_tokens": 23894,
    "total_output_tokens": 857
  },
  "created": "2026-05-08T10:44:43Z",
  "updated": "2026-05-08T10:44:43Z",
  "environment_id": "your-environment-id",
  "object": "interaction"
}

4. Multi-Turn Stateful Interaction Payload

4. 多轮有状态交互请求体

To continue an existing conversation statefully, specify the

previous_interaction_id

in the JSON payload:

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "store": true,
    "previous_interaction_id": "YOUR_PREVIOUS_INTERACTION_ID",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Can you elaborate on that?"
      }]
    }]
  }'

要继续现有对话的有状态会话，请在JSON请求体中指定

previous_interaction_id

：

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "store": true,
    "previous_interaction_id": "YOUR_PREVIOUS_INTERACTION_ID",
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Can you elaborate on that?"
      }]
    }]
  }'

5. Streaming Output Payload

5. 流式输出请求体

To stream updates in real time (Server-Sent Events format), pass

"stream": true

in the payload:

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "stream": true,
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Write a long story about space travel."
      }]
    }]
  }'

The endpoint will return a chunked stream where each event begins with

data:

containing JSON updates with the

event_type

and step contents.

How
curl
handles streaming: By default, when
"stream": true
is passed, the server responds with
Transfer-Encoding: chunked
and
Content-Type: text/event-stream
(Server-Sent Events).
curl
will automatically keep the connection open and print the incoming data chunks to
stdout
in real time as they are pushed by the server. The user does not need to poll or pull further; the complete sequence of events streams continuously until completion.

要实时流式获取更新（Server-Sent Events格式），请在请求体中传入

"stream": true

：

bash

curl -X POST "https://aiplatform.googleapis.com/v1beta1/projects/${PROJECT_ID}/locations/global/interactions" \
  -H "Authorization: Bearer ${ACCESS_TOKEN}" \
  -H "Content-Type: application/json" \
  -d '{
    "agent": "'"${AGENT_ID}"'",
    "stream": true,
    "input": [{
      "role": "user",
      "content": [{
        "type": "text",
        "text": "Write a long story about space travel."
      }]
    }]
  }'

端点会返回分片流，每个事件以

data:

开头，包含带有

event_type

和步骤内容的JSON更新。

curl处理流式传输的方式： 默认情况下，当传入
"stream": true
时，服务器会返回
Transfer-Encoding: chunked
和
Content-Type: text/event-stream
（Server-Sent Events）。curl会自动保持连接打开，并在服务器推送数据时实时将传入的数据分片打印到
stdout
。用户无需轮询或进一步拉取，完整的事件序列会持续流式传输直至完成。

gemini-interactions-api

Original

Translation

Gemini Interactions API Skill

Gemini Interactions API 技能指南

1. Authentication

1. 身份认证

2. Client Initialization

2. 客户端初始化

Option A: Environment Variables (Recommended)

选项A：环境变量（推荐）

Python

Python

The SDK automatically picks up the environment variables

SDK会自动读取环境变量

TypeScript/JavaScript

TypeScript/JavaScript

Option B: Explicit Inline Parameters

选项B：显式内联参数

Python

Python

TypeScript/JavaScript

TypeScript/JavaScript

3. Core Interactions API Usage

3. Interactions API核心用法

Quick Start (Single-Turn)

快速入门（单轮对话）

Python

Python

Output text is located under steps

输出文本位于steps下

TypeScript/JavaScript

TypeScript/JavaScript

Stateful Conversation (Multi-Turn)

有状态对话（多轮对话）

Python

Python

Turn 1: Introduce ourselves

第一轮：自我介绍

Turn 2: Refer back to the stored turn state

第二轮：引用已存储的对话状态

TypeScript/JavaScript

TypeScript/JavaScript

Real-Time Streaming

实时流式传输

Python

Python

TypeScript/JavaScript

TypeScript/JavaScript

Structured Output (Pydantic / Polymorphic response_format)

结构化输出（Pydantic / 多态response_format）

Python

Python

The text will be a valid JSON matching the Book schema

返回的文本是符合Book schema的有效JSON

TypeScript/JavaScript

TypeScript/JavaScript

Function Calling (Agent Tool Use)

函数调用（代理工具使用）

Python

Python

Turn 1: Pass tools to the model

第一轮：向模型传递工具

Check if the model requested a function call

检查模型是否请求了函数调用

TypeScript/JavaScript

TypeScript/JavaScript

4. Accessing the Interactions API via REST

4. 通过REST访问Interactions API

1. REST Endpoint

1. REST端点

2. Set up Variables & Authentication Header

2. 设置变量与认证头

3. Single-Turn Interaction Payload

3. 单轮交互请求体

Response Example

响应示例

4. Multi-Turn Stateful Interaction Payload

4. 多轮有状态交互请求体

5. Streaming Output Payload

Structured Output (Pydantic / Polymorphic
`response_format`
)

结构化输出（Pydantic / 多态
`response_format`
）