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feat: responses api example
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‎volcenginesdkarkruntime/resources/responses/responses.py‎

Lines changed: 4 additions & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -54,7 +54,10 @@ def _add_beta_headers(extra_headers: Headers | None = None, tools: Iterable[Tool
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if extra_headers is None:
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extra_headers = {}
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extra_headers["ark-beta-web-search"] = "true"
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break
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if tool_param.get("type", "") == "mcp":
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if extra_headers is None:
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extra_headers = {}
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extra_headers["ark-beta-mcp"] = "true"
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return extra_headers
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‎volcenginesdkexamples/volcenginesdkarkruntime/async_responses_create.py‎

Lines changed: 224 additions & 0 deletions
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@@ -0,0 +1,224 @@
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import asyncio
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from volcenginesdkarkruntime import AsyncArk
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from volcenginesdkarkruntime.types.responses.response_completed_event import ResponseCompletedEvent
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from volcenginesdkarkruntime.types.responses.response_output_item_done_event import ResponseOutputItemDoneEvent
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from volcenginesdkarkruntime.types.responses.response_function_tool_call import ResponseFunctionToolCall
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from volcenginesdkarkruntime.types.responses.response_mcp_item import McpApprovalRequest
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"""
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示例代码：演示 Responses API 的常见用法
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-------------------------------------------------
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1. 多轮对话中使用缓存 (caching)
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2. 调用外部函数 (function calling)
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3. Web 搜索工具 (web search)
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4. 使用MCP工具 (MCP)
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"""
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client = AsyncArk(api_key="${YOUR_API_KEY}")
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async def main():
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# ==========================================================
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# 示例 1：多轮对话，开启 caching
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# ==========================================================
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print("Example 1: Use caching for multi-round chat")
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# ---------- 第 1 轮 ----------
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# 说明：开启 caching，store=True 表示把对话存储在服务端，以便后续引用
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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input=[
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{"role": "system", "content": "你是豆包，是由字节跳动开发的 AI 人工智能助手"},
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{"role": "user", "content": "你好"},
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],
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caching={
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"type": "enabled",
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},
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store=True,
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stream=True
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)
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response_id = ""
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async for event in stream:
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print(event)
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if isinstance(event, ResponseCompletedEvent):
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response_id = event.response.id
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# ---------- 第 2 轮 ----------
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# 说明：通过 previous_response_id 关联上一轮的上下文
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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previous_response_id=response_id,
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input=[
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{"role": "user", "content": "你是谁"},
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],
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caching={
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"type": "enabled",
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},
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store=True,
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stream=True
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)
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async for event in stream:
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print(event)
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# ==========================================================
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# 示例 2：函数调用 (Function Calling)
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# ==========================================================
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print("Example 2: Use responses API for function calling")
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# ---------- 第 1 轮 ----------
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# 用户询问北京天气，模型会触发工具调用
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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input=[
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{"role": "user", "content": "请问北京今天天气怎么样"},
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],
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tools=[
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{
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"type": "function",
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"name": "get_current_weather",
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"description": "获取当前城市的天气",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "城市名称，例如北京"
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},
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"unit": {
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"type": "string",
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"description": "温度单位，例如摄氏度"
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}
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},
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"required": ["location"]
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}
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}
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],
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caching={
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"type": "enabled",
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},
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store=True,
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stream=True
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)
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call_id = ""
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response_id = ""
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async for event in stream:
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print(event)
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if isinstance(event, ResponseCompletedEvent):
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response_id = event.response.id
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if isinstance(event, ResponseOutputItemDoneEvent) and isinstance(event.item, ResponseFunctionToolCall):
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call_id = event.item.call_id
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# ---------- 第 2 轮 ----------
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# 把函数返回结果传回模型，让它继续生成最终回答
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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previous_response_id=response_id,
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input=[
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{
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"type": "function_call_output",
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"call_id": call_id,
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"output": "{\"temperature\": \"30\"}",
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},
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],
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caching={
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"type": "enabled",
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},
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store=True,
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stream=True
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)
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async for event in stream:
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print(event)
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# ==========================================================
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# 示例 3：Web 搜索工具
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# ==========================================================
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print("Example 3: Use responses API for web search")
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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input=[
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{"role": "user", "content": "今天的新闻"},
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],
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tools=[
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{
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"type": "web_search",
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"limit": 3,
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"sources": ["toutiao"],
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"user_location": {
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"type": "approximate",
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"city": "北京",
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"country": "中国",
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"region": "北京",
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}
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}
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],
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store=True,
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stream=True
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)
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async for event in stream:
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print(event)
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# ==========================================================
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# 示例 4：使用 MCP
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# ==========================================================
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# ---------- 第 1 轮 ----------
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# 用户询问repo信息，模型会触发mcp工具调用
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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input=[{
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"role": "user",
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"content": [
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{
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"type": "input_text",
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"text": "查看这个 repo的文档 expressjs/express "
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}
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]
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}],
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tools=[
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{
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"type": "mcp",
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"server_label": "deepwiki-test",
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"server_url": "https://mcp.deepwiki.com/mcp",
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"require_approval": "always"
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}
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],
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store=True,
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stream=True,
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)
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approval_id = ""
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response_id = ""
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async for event in stream:
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print(event)
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if isinstance(event, ResponseCompletedEvent):
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response_id = event.response.id
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if isinstance(event, ResponseOutputItemDoneEvent) and isinstance(event.item, McpApprovalRequest):
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approval_id = event.item.id
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# ---------- 第 2 轮 ----------
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# 用户同意mcp工具调用，模型会继续生成最终回答
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stream = await client.responses.create(
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model="${YOUR_ENDPOINT_ID}",
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input=[
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{
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"type": "mcp_approval_response",
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"approval_request_id": approval_id,
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"approve": True
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}
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],
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previous_response_id=response_id,
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tools=[
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{
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"type": "mcp",
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"server_label": "deepwiki-test",
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"server_url": "https://mcp.deepwiki.com/mcp",
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"require_approval": "always"
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}
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],
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store=True,
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stream=True,
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)
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async for event in stream:
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print(event)
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if __name__ == "__main__":
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asyncio.run(main())

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