https://microsoft.github.io/autogen/docs/Getting-Started/
Part1 - P - Multi-Agent Conversation
demo for direct run + iteratively debug
input
# create an AssistantAgent named "assistant"
assistant = autogen.AssistantAgent(
name="assistant",
llm_config={
"seed": 42, # seed for caching and reproducibility
"config_list": config_list, # a list of OpenAI API configurations
"temperature": 0, # temperature for sampling
}, # configuration for autogen's enhanced inference API which is compatible with OpenAI API
)
# create a UserProxyAgent instance named "user_proxy"
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "coding",
"use_docker": False, # set to True or image name like "python:3" to use docker
},
)
# the assistant receives a message from the user_proxy, which contains the task description
user_proxy.initiate_chat(
assistant,
message="""give me the solution code in python and execute it for me; (you need to write a more complete test set by yourself, and test correctness; if not debug your code): 4. Median of Two Sorted Arrays
Hard
Topics
Companies
Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays.
The overall run time complexity should be O(log (m+n)).
Example 1:
Input: nums1 = [1,3], nums2 = [2]
Output: 2.00000
Explanation: merged array = [1,2,3] and median is 2.
Example 2:
Input: nums1 = [1,2], nums2 = [3,4]
Output: 2.50000
Explanation: merged array = [1,2,3,4] and median is (2 + 3) / 2 = 2.5.
Constraints:
nums1.length == m
nums2.length == n
0 <= m <= 1000
0 <= n <= 1000
1 <= m + n <= 2000
-10^6 <= nums1[i], nums2[i] <= 10^6""",
)output
user_proxy (to assistant): give me the solution code in python and execute it for me; (you need to write a more complete test set by yourself, and test correctness; if not debug your code): 4. Median of Two Sorted Arrays Hard Topics Companies Given two sorted arrays nums1 and nums2 of size m and n respectively, return the median of the two sorted arrays. The overall run time complexity should be O(log (m+n)). Example 1: Input: nums1 = [1,3], nums2 = [2] Output: 2.00000 Explanation: merged array = [1,2,3] and median is 2. Example 2: Input: nums1 = [1,2], nums2 = [3,4] Output: 2.50000 Explanation: merged array = [1,2,3,4] and median is (2 + 3) / 2 = 2.5. Constraints: nums1.length == m nums2.length == n 0 <= m <= 1000 0 <= n <= 1000 1 <= m + n <= 2000 -10^6 <= nums1[i], nums2[i] <= 10^6 -------------------------------------------------------------------------------- assistant (to user_proxy): Here is a Python solution for the problem. This solution uses binary search to find the median in O(log(min(m, n))) time complexity. It first checks which array is smaller and performs binary search on it. It calculates the partition of both arrays such that elements on the left side are smaller than elements on the right side. If the total length of both arrays is even, it returns the average of the two middle elements, otherwise it returns the middle element. ```python def findMedianSortedArrays(nums1, nums2): if len(nums1) > len(nums2): nums1, nums2 = nums2, nums1 x, y = len(nums1), len(nums2) start = 0 end = x while start <= end: partitionX = (start + end) // 2 partitionY = ((x + y + 1) // 2) - partitionX maxLeftX = float('-inf') if partitionX == 0 else nums1[partitionX - 1] minRightX = float('inf') if partitionX == x else nums1[partitionX] maxLeftY = float('-inf') if partitionY == 0 else nums2[partitionY - 1] minRightY = float('inf') if partitionY == y else nums2[partitionY] if maxLeftX <= minRightY and maxLeftY <= minRightX: if (x + y) % 2 == 0: return (max(maxLeftX, maxLeftY) + min(minRightX, minRightY)) / 2 else: return max(maxLeftX, maxLeftY) elif maxLeftX > minRightY: end = partitionX - 1 else: start = partitionX + 1 ``` Now, let's test this function with some test cases: ```python print(findMedianSortedArrays([1, 3], [2])) # Expected output: 2.0 print(findMedianSortedArrays([1, 2], [3, 4])) # Expected output: 2.5 print(findMedianSortedArrays([1, 2, 3], [4, 5, 6])) # Expected output: 3.5 print(findMedianSortedArrays([1, 2, 3, 4], [5, 6, 7, 8])) # Expected output: 4.5 print(findMedianSortedArrays([1, 2, 3, 4, 5], [6, 7, 8, 9, 10])) # Expected output: 5.5 ``` Please run the above code and let me know the results. -------------------------------------------------------------------------------- >>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...
WARNING:root:SIGALRM is not supported on Windows. No timeout will be enforced.
>>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...
WARNING:root:SIGALRM is not supported on Windows. No timeout will be enforced.
user_proxy (to assistant): exitcode: 1 (execution failed) Code output: Traceback (most recent call last): File "", line 1, in <module> print(findMedianSortedArrays([1, 3], [2])) # Expected output: 2.0 NameError: name 'findMedianSortedArrays' is not defined -------------------------------------------------------------------------------- assistant (to user_proxy): I apologize for the confusion. It seems like the function definition and the test cases were not executed together. Please run the following code which includes both the function definition and the test cases. ```python def findMedianSortedArrays(nums1, nums2): if len(nums1) > len(nums2): nums1, nums2 = nums2, nums1 x, y = len(nums1), len(nums2) start = 0 end = x while start <= end: partitionX = (start + end) // 2 partitionY = ((x + y + 1) // 2) - partitionX maxLeftX = float('-inf') if partitionX == 0 else nums1[partitionX - 1] minRightX = float('inf') if partitionX == x else nums1[partitionX] maxLeftY = float('-inf') if partitionY == 0 else nums2[partitionY - 1] minRightY = float('inf') if partitionY == y else nums2[partitionY] if maxLeftX <= minRightY and maxLeftY <= minRightX: if (x + y) % 2 == 0: return (max(maxLeftX, maxLeftY) + min(minRightX, minRightY)) / 2 else: return max(maxLeftX, maxLeftY) elif maxLeftX > minRightY: end = partitionX - 1 else: start = partitionX + 1 # Test cases print(findMedianSortedArrays([1, 3], [2])) # Expected output: 2.0 print(findMedianSortedArrays([1, 2], [3, 4])) # Expected output: 2.5 print(findMedianSortedArrays([1, 2, 3], [4, 5, 6])) # Expected output: 3.5 print(findMedianSortedArrays([1, 2, 3, 4], [5, 6, 7, 8])) # Expected output: 4.5 print(findMedianSortedArrays([1, 2, 3, 4, 5], [6, 7, 8, 9, 10])) # Expected output: 5.5 ``` -------------------------------------------------------------------------------- >>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)...
WARNING:root:SIGALRM is not supported on Windows. No timeout will be enforced.
user_proxy (to assistant): exitcode: 0 (execution succeeded) Code output: 2 2.5 3.5 4.5 5.5 -------------------------------------------------------------------------------- assistant (to user_proxy): Great! The code has executed successfully and the output matches the expected results for all test cases. This confirms that the function `findMedianSortedArrays` is working correctly to find the median of two sorted arrays. If you have any other tasks or questions, feel free to ask. TERMINATE --------------------------------------------------------------------------------
demo for auto env install
input
# create an AssistantAgent named "assistant"
assistant = autogen.AssistantAgent(
name="assistant",
llm_config={
"seed": 42, # seed for caching and reproducibility
"config_list": config_list, # a list of OpenAI API configurations
"temperature": 0, # temperature for sampling
}, # configuration for autogen's enhanced inference API which is compatible with OpenAI API
)
# create a UserProxyAgent instance named "user_proxy"
user_proxy = autogen.UserProxyAgent(
name="user_proxy",
human_input_mode="NEVER",
max_consecutive_auto_reply=10,
is_termination_msg=lambda x: x.get("content", "").rstrip().endswith("TERMINATE"),
code_execution_config={
"work_dir": "coding",
"use_docker": False, # set to True or image name like "python:3" to use docker
},
)
# the assistant receives a message from the user_proxy, which contains the task description
user_proxy.initiate_chat(
assistant,
message="""Uninstall any existing version of matplotlib, then reinstall it, and create a Python script to plot a sine wave using matplotlib.
""",
)output
user_proxy (to assistant): Uninstall any existing version of matplotlib, then reinstall it, and create a Python script to plot a sine wave using matplotlib. -------------------------------------------------------------------------------- assistant (to user_proxy): To uninstall any existing version of matplotlib and reinstall it, you can use pip, which is a package manager for Python. Here is the shell script to do that: ```sh pip uninstall matplotlib -y pip install matplotlib ``` After reinstalling matplotlib, you can create a Python script to plot a sine wave. Here is the Python code to do that: ```python # filename: plot_sine_wave.py import matplotlib.pyplot as plt import numpy as np # Create an array of x values from 0 to 2 Pi x = np.linspace(0, 2 * np.pi, 100) # Create an array of y values representing the sine of x y = np.sin(x) # Create a plot using matplotlib plt.plot(x, y) # Display the plot plt.show() ``` You can run this script using the command `python plot_sine_wave.py` in your terminal. This will display a plot of a sine wave. -------------------------------------------------------------------------------- >>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is sh)...
WARNING:root:SIGALRM is not supported on Windows. No timeout will be enforced.
>>>>>>>> EXECUTING CODE BLOCK 1 (inferred language is python)...
WARNING:root:SIGALRM is not supported on Windows. No timeout will be enforced.
user_proxy (to assistant): exitcode: 0 (execution succeeded) Code output: Found existing installation: matplotlib 3.5.1 Uninstalling matplotlib-3.5.1: Successfully uninstalled matplotlib-3.5.1 Collecting matplotlib Using cached matplotlib-3.8.0-cp39-cp39-win_amd64.whl (7.6 MB) Collecting contourpy>=1.0.1 Downloading contourpy-1.1.1-cp39-cp39-win_amd64.whl (435 kB) -------------------------------------- 436.0/436.0 kB 9.1 MB/s eta 0:00:00 Requirement already satisfied: fonttools>=4.22.0 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (4.25.0) Requirement already satisfied: numpy<2,>=1.21 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (1.21.6) Requirement already satisfied: python-dateutil>=2.7 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (2.8.2) Requirement already satisfied: pillow>=6.2.0 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (9.4.0) Requirement already satisfied: packaging>=20.0 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (21.3) Requirement already satisfied: pyparsing>=2.3.1 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (3.0.9) Collecting importlib-resources>=3.2.0 Downloading importlib_resources-6.1.0-py3-none-any.whl (33 kB) Requirement already satisfied: kiwisolver>=1.0.1 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (1.4.2) Requirement already satisfied: cycler>=0.10 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from matplotlib) (0.11.0) Requirement already satisfied: zipp>=3.1.0 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from importlib-resources>=3.2.0->matplotlib) (3.8.0) Requirement already satisfied: six>=1.5 in [d:\anaconda3\lib\site-packages](file:///D:/anaconda3/lib/site-packages) (from python-dateutil>=2.7->matplotlib) (1.16.0) Installing collected packages: importlib-resources, contourpy, matplotlib Successfully installed contourpy-1.1.1 importlib-resources-6.1.0 matplotlib-3.8.0 Figure(640x480) -------------------------------------------------------------------------------- assistant (to user_proxy): Great! The matplotlib package has been successfully uninstalled and reinstalled, and the Python script has successfully plotted a sine wave. You can verify this by the "Figure(640x480)" output, which indicates that a figure of size 640x480 has been created. However, as this is a text-based interface, the actual plot cannot be displayed here. You should be able to see the plot on your local machine where the script was run. TERMINATE --------------------------------------------------------------------------------