chore: import upstream snapshot with attribution

scripts/model_examples/dashscope_call.py

@@ -0,0 +1,185 @@
+# -*- coding: utf-8 -*-
+"""Examples of DashScope (Alibaba) model calls."""
+import asyncio
+import json
+import os
+
+from pydantic import BaseModel, Field
+
+from _utils import stream_and_collect
+from agentscope.message import (
+    Msg,
+    ToolCallBlock,
+    ToolResultBlock,
+    ToolResultState,
+    TextBlock,
+)
+from agentscope.model import DashScopeChatModel
+from agentscope.credential import DashScopeCredential
+from agentscope.tool import Toolkit, ToolChoice, FunctionTool
+
+
+# ---------------------------------------------------------------------------
+# Example 1: Simple user message (streaming)
+# ---------------------------------------------------------------------------
+
+
+async def example_simple_call() -> None:
+    """Call the DashScope model with a simple text message."""
+    model = DashScopeChatModel(
+        credential=DashScopeCredential(
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+        ),
+        model="qwen3.5-plus",
+        stream=True,
+        context_size=1_000_000,
+        parameters=DashScopeChatModel.Parameters(thinking_enable=True),
+    )
+
+    msgs = [
+        Msg(
+            name="user",
+            content=[TextBlock(text="What is 1 + 1? Answer briefly.")],
+            role="user",
+        ),
+    ]
+
+    print("=== Simple Call ===")
+    await stream_and_collect(await model(msgs))
+
+
+# ---------------------------------------------------------------------------
+# Example 2: Tool calling (streaming)
+# ---------------------------------------------------------------------------
+
+
+def get_weather(city: str) -> str:
+    """Get the current weather for a city.
+
+    Args:
+        city: The city name to query the weather for.
+
+    Returns:
+        A description of the current weather.
+    """
+    return f"The weather in {city} is sunny and 25°C."
+
+
+async def example_tool_call() -> None:
+    """Call the DashScope model with tool calling enabled.
+
+    Uses qwen3-max which supports both thinking mode and tool calling.
+    """
+    toolkit = Toolkit(tools=[FunctionTool(get_weather)])
+    tools = await toolkit.get_tool_schemas()
+
+    model = DashScopeChatModel(
+        credential=DashScopeCredential(
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+        ),
+        model="qwen3.5-plus",
+        stream=True,
+        context_size=1_000_000,
+        parameters=DashScopeChatModel.Parameters(thinking_enable=True),
+    )
+
+    msgs = [
+        Msg(
+            name="user",
+            content=[TextBlock(text="What is the weather in Beijing?")],
+            role="user",
+        ),
+    ]
+
+    # First call: model decides to call a tool
+    print("=== Tool Call - Round 1 ===")
+    response = await stream_and_collect(
+        await model(msgs, tools=tools, tool_choice=ToolChoice(mode="auto")),
+    )
+    print(response)
+
+    tool_calls = [b for b in response.content if isinstance(b, ToolCallBlock)]
+    if tool_calls:
+        tool_result_blocks = []
+        for tool_call in tool_calls:
+            args = json.loads(tool_call.input)
+            result = get_weather(**args)
+            tool_result_blocks.append(
+                ToolResultBlock(
+                    id=tool_call.id,
+                    name=tool_call.name,
+                    output=result,
+                    state=ToolResultState.SUCCESS,
+                ),
+            )
+
+        assistant_msg = Msg(
+            name="assistant",
+            content=response.content,
+            role="assistant",
+        )
+        tool_result_msg = Msg(
+            name="tool",
+            content=tool_result_blocks,
+            role="assistant",
+        )
+        msgs = msgs + [assistant_msg, tool_result_msg]
+
+        print("=== Tool Call - Round 2 (Final) ===")
+        await stream_and_collect(await model(msgs))
+
+
+# ---------------------------------------------------------------------------
+# Example 3: Structured output
+# ---------------------------------------------------------------------------
+
+
+class MathSolution(BaseModel):
+    """Structured solution to a math problem."""
+
+    problem: str = Field(description="The original problem statement")
+    answer: float = Field(description="The final numeric answer")
+    steps: list[str] = Field(
+        description="Step-by-step reasoning leading to the answer",
+    )
+
+
+async def example_structured_output() -> None:
+    """Call the DashScope model and force a structured (JSON) output."""
+    model = DashScopeChatModel(
+        credential=DashScopeCredential(
+            api_key=os.environ["DASHSCOPE_API_KEY"],
+        ),
+        model="qwen3.5-plus",
+        stream=True,
+        context_size=1_000_000,
+        parameters=DashScopeChatModel.Parameters(thinking_enable=True),
+    )
+
+    msgs = [
+        Msg(
+            name="user",
+            content=[
+                TextBlock(
+                    text=(
+                        "Solve this: A train travels at 60 km/h for "
+                        "2.5 hours. How far does it travel in km?"
+                    ),
+                ),
+            ],
+            role="user",
+        ),
+    ]
+
+    print("=== Structured Output ===")
+    response = await model.generate_structured_output(
+        msgs,
+        structured_model=MathSolution,
+    )
+    print(response.content)
+
+
+if __name__ == "__main__":
+    asyncio.run(example_simple_call())
+    asyncio.run(example_tool_call())
+    asyncio.run(example_structured_output())