Note
Go to the end to download the full example code.
Routing¶
There are two ways to implement routing in AgentScope, both simple and easy to implement:
Routing by structured output
Routing by tool calls
Tip
Considering there is no unified standard/definition for agent routing, we follow the setting in Building effective agents
Routing by Structured Output¶
By this way, we can directly use the structured output of the agent to determine which agent to route the message to.
Initialize a routing agent
import asyncio
import json
import os
from typing import Literal
from pydantic import BaseModel, Field
from agentscope.agent import ReActAgent
from agentscope.formatter import DashScopeChatFormatter
from agentscope.memory import InMemoryMemory
from agentscope.message import Msg
from agentscope.model import DashScopeChatModel
from agentscope.tool import Toolkit, ToolResponse
router = ReActAgent(
name="Router",
sys_prompt="You're a routing agent. Your target is to route the user query to the right follow-up task.",
model=DashScopeChatModel(
model_name="qwen-max",
api_key=os.environ["DASHSCOPE_API_KEY"],
stream=False,
),
formatter=DashScopeChatFormatter(),
)
# Use structured output to specify the routing task
class RoutingChoice(BaseModel):
your_choice: Literal[
"Content Generation",
"Programming",
"Information Retrieval",
None,
] = Field(
description="Choose the right follow-up task, and choose ``None`` if the task is too simple or no suitable task",
)
task_description: str | None = Field(
description="The task description",
default=None,
)
async def example_router_explicit() -> None:
"""Example of explicit routing with structured output."""
msg_user = Msg(
"user",
"Help me to write a poem",
"user",
)
# Route the query
msg_res = await router(
msg_user,
structured_model=RoutingChoice,
)
# The structured output is stored in the metadata field
print("The structured output:")
print(json.dumps(msg_res.metadata, indent=4, ensure_ascii=False))
asyncio.run(example_router_explicit())
/home/runner/work/agentscope/agentscope/src/agentscope/model/_dashscope_model.py:232: DeprecationWarning: 'required' is not supported by DashScope API. It will be converted to 'auto'.
warnings.warn(
Router: {
"type": "tool_use",
"name": "generate_response",
"input": {
"your_choice": "Content Generation",
"task_description": "Write a poem for the user"
},
"id": "call_c9f45eb265c94ae1b6d5c5"
}
system: {
"type": "tool_result",
"id": "call_c9f45eb265c94ae1b6d5c5",
"name": "generate_response",
"output": [
{
"type": "text",
"text": "Successfully generated response."
}
]
}
Router: I understand you'd like some help with writing a poem. I can certainly assist with that! Could you please share more details about the theme, style, or any specific elements you would like to include in your poem? This will help me tailor the poem to your preferences.
The structured output:
{
"your_choice": "Content Generation",
"task_description": "Write a poem for the user"
}
Routing by Tool Calls¶
Another way is to wrap the downstream agents into a tool function, so that the routing agent decides which tool to call based on the user query.
We first define several tool functions:
async def generate_python(demand: str) -> ToolResponse:
"""Generate Python code based on the demand.
Args:
demand (``str``):
The demand for the Python code.
"""
# An example demand agent
python_agent = ReActAgent(
name="PythonAgent",
sys_prompt="You're a Python expert, your target is to generate Python code based on the demand.",
model=DashScopeChatModel(
model_name="qwen-max",
api_key=os.environ["DASHSCOPE_API_KEY"],
stream=False,
),
memory=InMemoryMemory(),
formatter=DashScopeChatFormatter(),
toolkit=Toolkit(),
)
msg_res = await python_agent(Msg("user", demand, "user"))
return ToolResponse(
content=msg_res.get_content_blocks("text"),
)
# Fake some other tool functions for demonstration purposes
async def generate_poem(demand: str) -> ToolResponse:
"""Generate a poem based on the demand.
Args:
demand (``str``):
The demand for the poem.
"""
pass
async def web_search(query: str) -> ToolResponse:
"""Search the web for the query.
Args:
query (``str``):
The query to search.
"""
pass
After that, we define a routing agent and equip it with the above tool functions.
toolkit = Toolkit()
toolkit.register_tool_function(generate_python)
toolkit.register_tool_function(generate_poem)
toolkit.register_tool_function(web_search)
# Initialize the routing agent with the toolkit
router_implicit = ReActAgent(
name="Router",
sys_prompt="You're a routing agent. Your target is to route the user query to the right follow-up task.",
model=DashScopeChatModel(
model_name="qwen-max",
api_key=os.environ["DASHSCOPE_API_KEY"],
stream=False,
),
formatter=DashScopeChatFormatter(),
toolkit=toolkit,
memory=InMemoryMemory(),
)
async def example_router_implicit() -> None:
"""Example of implicit routing with tool calls."""
msg_user = Msg(
"user",
"Help me to generate a quick sort function in Python",
"user",
)
# Route the query
await router_implicit(msg_user)
asyncio.run(example_router_implicit())
Router: {
"type": "tool_use",
"name": "generate_python",
"input": {
"demand": "a quick sort function"
},
"id": "call_d0a63ba295d24758b9278c"
}
PythonAgent: Certainly! Quick sort is a popular and efficient sorting algorithm that uses a divide-and-conquer approach. Here's a Python implementation of the quick sort function:
```python
def quick_sort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
# Example usage:
arr = [3, 6, 8, 10, 1, 2, 1]
sorted_arr = quick_sort(arr)
print("Sorted array:", sorted_arr)
```
### Explanation:
- **Base Case**: If the array has 0 or 1 elements, it is already sorted, so we return it as is.
- **Pivot Selection**: We choose the middle element of the array as the pivot.
- **Partitioning**: We create three lists:
- `left`: All elements less than the pivot.
- `middle`: All elements equal to the pivot.
- `right`: All elements greater than the pivot.
- **Recursive Sorting**: We recursively apply the quick sort function to the `left` and `right` lists and concatenate the results with the `middle` list.
This implementation is simple and easy to understand, but it may not be the most efficient in terms of space complexity due to the use of additional lists. For an in-place version, you can modify the function to sort the array in place. Here's an in-place version:
```python
def quick_sort_in_place(arr, low, high):
if low < high:
pi = partition(arr, low, high)
quick_sort_in_place(arr, low, pi - 1)
quick_sort_in_place(arr, pi + 1, high)
def partition(arr, low, high):
pivot = arr[high]
i = low - 1
for j in range(low, high):
if arr[j] < pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]
arr[i + 1], arr[high] = arr[high], arr[i + 1]
return i + 1
# Example usage:
arr = [3, 6, 8, 10, 1, 2, 1]
quick_sort_in_place(arr, 0, len(arr) - 1)
print("Sorted array:", arr)
```
### Explanation:
- **In-Place Partitioning**: The `partition` function rearranges the elements in the array such that all elements less than the pivot are on the left, and all elements greater than the pivot are on the right.
- **Recursive Sorting**: The `quick_sort_in_place` function recursively sorts the subarrays before and after the pivot.
Both versions will sort the array, but the in-place version is more space-efficient.
system: {
"type": "tool_result",
"id": "call_d0a63ba295d24758b9278c",
"name": "generate_python",
"output": [
{
"type": "text",
"text": "Certainly! Quick sort is a popular and efficient sorting algorithm that uses a divide-and-conquer approach. Here's a Python implementation of the quick sort function:\n\n```python\ndef quick_sort(arr):\n if len(arr) <= 1:\n return arr\n else:\n pivot = arr[len(arr) // 2]\n left = [x for x in arr if x < pivot]\n middle = [x for x in arr if x == pivot]\n right = [x for x in arr if x > pivot]\n return quick_sort(left) + middle + quick_sort(right)\n\n# Example usage:\narr = [3, 6, 8, 10, 1, 2, 1]\nsorted_arr = quick_sort(arr)\nprint(\"Sorted array:\", sorted_arr)\n```\n\n### Explanation:\n- **Base Case**: If the array has 0 or 1 elements, it is already sorted, so we return it as is.\n- **Pivot Selection**: We choose the middle element of the array as the pivot.\n- **Partitioning**: We create three lists:\n - `left`: All elements less than the pivot.\n - `middle`: All elements equal to the pivot.\n - `right`: All elements greater than the pivot.\n- **Recursive Sorting**: We recursively apply the quick sort function to the `left` and `right` lists and concatenate the results with the `middle` list.\n\nThis implementation is simple and easy to understand, but it may not be the most efficient in terms of space complexity due to the use of additional lists. For an in-place version, you can modify the function to sort the array in place. Here's an in-place version:\n\n```python\ndef quick_sort_in_place(arr, low, high):\n if low < high:\n pi = partition(arr, low, high)\n quick_sort_in_place(arr, low, pi - 1)\n quick_sort_in_place(arr, pi + 1, high)\n\ndef partition(arr, low, high):\n pivot = arr[high]\n i = low - 1\n for j in range(low, high):\n if arr[j] < pivot:\n i += 1\n arr[i], arr[j] = arr[j], arr[i]\n arr[i + 1], arr[high] = arr[high], arr[i + 1]\n return i + 1\n\n# Example usage:\narr = [3, 6, 8, 10, 1, 2, 1]\nquick_sort_in_place(arr, 0, len(arr) - 1)\nprint(\"Sorted array:\", arr)\n```\n\n### Explanation:\n- **In-Place Partitioning**: The `partition` function rearranges the elements in the array such that all elements less than the pivot are on the left, and all elements greater than the pivot are on the right.\n- **Recursive Sorting**: The `quick_sort_in_place` function recursively sorts the subarrays before and after the pivot.\n\nBoth versions will sort the array, but the in-place version is more space-efficient."
}
]
}
Router: The quick sort function in Python has been generated for you. Below is the simple version of the quick sort algorithm, which uses additional space for the `left`, `middle`, and `right` lists:
```python
def quick_sort(arr):
if len(arr) <= 1:
return arr
else:
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quick_sort(left) + middle + quick_sort(right)
# Example usage:
arr = [3, 6, 8, 10, 1, 2, 1]
sorted_arr = quick_sort(arr)
print("Sorted array:", sorted_arr)
```
Additionally, an in-place version of the quick sort algorithm was also provided, which sorts the array without using extra space for new lists:
```python
def quick_sort_in_place(arr, low, high):
if low < high:
pi = partition(arr, low, high)
quick_sort_in_place(arr, low, pi - 1)
quick_sort_in_place(arr, pi + 1, high)
def partition(arr, low, high):
pivot = arr[high]
i = low - 1
for j in range(low, high):
if arr[j] < pivot:
i += 1
arr[i], arr[j] = arr[j], arr[i]
arr[i + 1], arr[high] = arr[high], arr[i + 1]
return i + 1
# Example usage:
arr = [3, 6, 8, 10, 1, 2, 1]
quick_sort_in_place(arr, 0, len(arr) - 1)
print("Sorted array:", arr)
```
You can use either of these functions to sort a list. The in-place version is more space-efficient, while the first version is easier to understand.
Total running time of the script: (1 minutes 12.105 seconds)