Spaces:
Paused
Paused
File size: 5,546 Bytes
0b9dc2e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 | # -*- 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())
|