How to Use AutoGen Without Relying on OpenAI or LM Studio?

Introduction

Are you ready to create your AI team without relying on OpenAI and LM studio? No more breaking the bank or downloading apps. From setting up llama-cpp-python to exploring the power of local LLMs with the help of autogen framework. Get ready to unlock the full potential of Autogen without depending on OpenAI API.

Learning Objectives

Before we dive into the details, let’s outline the key learning objectives of this article:

• Learn how to evaluate and compare different AI libraries and tools.
• Explore how llama-cpp-python serves as a drop-in replacement for the OpenAI API.
• Apply the acquired knowledge in two real-world use cases: Building an algorithm mentor team and Automating financial chart generation.
• Explore AutoGen’s improved user experience with instant code execution results through integrated IPython.

This article was published as a part of the Data Science Blogathon.

Meet Your Tools: Llama-cpp-python, AutoGen, and Local LLMs

But what’s so special about this tech toolkit, you ask? Llama-cpp-python is your gateway to running LLMs locally, including big names like LLaMA. It’s like having AI superstars right on your computer, and with support for different BLAS backends, the speed is out of this world!

AutoGen

AutoGen is a unified multi-agent conversation framework that acts as a high-level abstraction for using foundation models. It combines capable, customizable, and conversable agents that integrate LLMs, tools, and human participants via automated chat. It enables agents to communicate and work together autonomously, effectively streamlining complex tasks and automating workflows.

If you’re eager to delve deeper into the capabilities of AutoGen and explore how it facilitates strategic AI team building, look no further than our dedicated blog: “Strategic  AI Team Building Made Easy with AutoGen.” This comprehensive resource provides insights, use cases, and a closer look at how AutoGen can transform your approach to AI development.

Libraries/Tools

Some libraries/tools offer a web server that aims to act as a replacement for the OpenAI API.

Other options are available besides those above, but the best depends on your preferences and needs.

Llama-cpp-python

Llama-cpp-python is a Python binding for the llama.cpp library. It provides low-level access to the C API via the ctypes interface, high-level Python API for text completion, OpenAI-like API, and LangChain compatibility. It supports multiple BLAS backends for faster processing and also supports hardware acceleration.

llama-cpp-python offers a web server that aims to act as a drop-in replacement for the OpenAI API. This allows you to use llama.cpp compatible models with any OpenAI-compatible client (language libraries, services, etc.)

Development

For this project, we are going to use these tools and libraries:

• Google Colab
• Autogen
• llama-cpp-python
• Local LLMs
  • codellama-7b-instruct.Q5_K_M.gguf

Setting Up Your Development Environment

Create a Virtual Environment

Virtual environments is a good practice to isolate project-specific dependencies and avoid conflicts with system-wide packages. Here’s how to set up a Python environment:

 Venv

python -m venv env_name

• Activate the Virtual Environment:

\env_name\Scripts\activate

• On macOS and Linux:

source env_name/bin/activate

The following command will deactivate the current venv environment:

deactivate

Required Dependencies

1. Cloning the Repository

!git clone --recurse-submodules https://github.com/abetlen/llama-cpp-python.git

• –recurse-submodules: A flag indicating that submodules associated with the repository should also be cloned. Submodules are separate Git repositories embedded within the main repository.
• https://github.com/abetlen/llama-cpp-python.git: The URL of the Git repository to be cloned.

2. Upgrade pip

pip install --upgrade pip

3. Change Directory to ‘llama-cpp-python’

%cd llama-cpp-python

4. Modifying Default Token Generation Limit in llama-cpp-python Repository

1. Clone the “llama-cpp-python” repository to your local machine.
2. Navigate to the “llama-cpp” folder within the cloned repository.
3. Inside the “server” folder, locate the file named “app.py.”
4. Open “app.py” and scroll down to line 490 or find the “max_tokens_field”.
5. In the “max_tokens_field” section, find the line that looks like this:

max_tokens_field = Field(
    default=16, ge=1, description="The maximum number of tokens to generate."
)

6.   Change the default value from 16 to your desired value (e.g., 4096). For example:

max_tokens_field = Field(
    default=4096, ge=1, description="The maximum number of tokens to generate."
)

7.    Save the changes to the “app.py” file.

Following these steps, you’ll modify the default value for the “max_tokens_field” in the “app.py” file to 4096 or your specified value.

5. Installing Dependencies

In Google Colab, you typically work in a cloud-based environment with access to NVIDIA GPUs. Therefore, for optimal performance, consider using a BLAS library that is compatible with GPU acceleration. In the case of Google Colab, cuBLAS is a suitable choice, as it is optimized for NVIDIA GPUs, and Colab instances often come with NVIDIA GPU support.

!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python[server]

• CMAKE_ARGS=”-DLLAMA_CUBLAS=on”: Sets the CMake (Cross-Platform Make) configuration argument to enable CUBLAS, a library for GPU-accelerated linear algebra operations.
• FORCE_CMAKE=1: Forces the CMake configuration to ensure proper setup.
• pip install llama-cpp-python[server]: Installs the llama-cpp-python package along with additional dependencies specified for server functionality.

6. Installing an Editable package

cd llama-cpp-python
!pip install -e .
!pip install -e .[server]

• cd llama-cpp-python: Changes the current working directory to llama-cpp-python.
• pip install -e .: Installs the current directory as an editable package, allowing dynamic changes to the code without reinstalling.
• pip install -e .[server]: Similar to the previous command, but includes additional dependencies specified in the [server] extras.

7. Change Directory to ‘llama_cpp/’

cd llama_cpp/Introduction

•   cd llama_cpp/: Changes the current working directory to the llama_cpp subdirectory within the project.

8. Download a LLM model

!wget https://huggingface.co/TheBloke/
CodeLlama-7B-Instruct-GGUF/resolve/main/codellama-7b-instruct.Q5_K_M.gguf

• wget: A command-line utility for downloading files from the web.

9. Run the Server in the Background

• Batch Size (n_batch): The batch size is a hyperparameter that determines the number of training samples to work through before the model’s internal parameters are updated.
• GPU Layers (n_gpu_layers): The number of layers to offload to the GPU for computations is usually set based on your VRAM capacity. You can increase the number gradually until you find a sweet spot. This can help optimize your model’s performance by leveraging the GPU’s computational power.
• Context Size (n_ctx): The context size represents the maximum number of tokens the input sequence can be. Most models are trained with a context size of 2048. If you need to process a longer prompt, you can change it. However, reviewing a model’s context limit is advised against since it hasn’t been trained to account for data sets larger than its suggested context limit.

!python -m server --model codellama-7b-instruct.Q5_K_M.gguf --host 127.0.0.1 --n_ctx 2048 --n_batch 128 --n_gpu_layers 35 > server.log 2>&1 &

• python -m server: Executes the Python module named server.
• –model codellama-7b-instruct.Q5_K_M.gguf: Specifies the path to the downloaded model file.
• –host 127.0.0.1: Sets the host IP address to 127.0.0.1 (localhost).
• –n_ctx 2048: Configures the context size to 2048.
• –n_batch 128: Sets the batch size to 128.
• –n_gpu_layers 35: Specifies the number of GPU layers as 35.
• > server.log 2>&1 &: Redirects standard output and errors to a file named server.log and runs the process in the background.

These commands collectively set up the ‘llama-cpp-python’ package, configure it with specific options, install additional dependencies for server functionality, and initiate a server with a pre-trained model. For further reference, refer to official documentation.

Coding

Use Case 1: Build your Algorithm Mentor Team

This code snippet demonstrates the creation of agents using the `autogen` library for a scenario where a student seeks guidance from a Coding Mentor and an Algorithm Expert in implementing sorting algorithms in Python.

Here’s a breakdown of the code:

1. Import the autogen module:

import autogen

This imports the necessary functionality from the autogen library.

2. Define agent configurations:

config_list = [
    {
        "model": "codellama-7b-instruct.Q5_K_M.gguf",
        "api_base": "http://127.0.0.1:8000/v1",
        "api_type": "open_ai",
        "api_key": "NULL",  # Placeholder
    }
]

This sets up a configuration list specifying details like the model to use, API base URL, type of API, and API key (which is a placeholder in this case).

3. Create agents with different roles and configurations:

coding_mentor = autogen.AssistantAgent(
    name="CodingMentor",
    llm_config={
        "seed": 42,
        "config_list": config_list,
        "temperature": 0.7,
        "request_timeout": 1200,
    },
    system_message="Coding Mentor here! I
     can guide you through implementing 
     sorting algorithms in Python.",
)

This creates an agent named CodingMentor with a specific configuration for the language model (llm_config), including details like seed, model configuration list, temperature, and request timeout.

algorithm_expert = autogen.AssistantAgent(
    name="AlgorithmExpert",
    llm_config={
        "seed": 42,
        "config_list": config_list,
        "temperature": 0.7,
        "request_timeout": 1200,
    },
    system_message="Algorithm Expert. I 
    specialize in algorithms. Let's work 
    on implementing a sorting algorithm 
    together.",
)

Similarly, this creates another agent named AlgorithmExpert with similar configurations.

student = autogen.UserProxyAgent(
    name="Student",
    human_input_mode="ALWAYS",
    code_execution_config={"work_dir":"node"},
)

This creates a user proxy agent named Student with the human_input_mode set to “ALWAYS,” indicating that the agent will always expect human input.

4. Initiate a chat between the student and the Coding Mentor:

student.initiate_chat(
    coding_mentor,
    message="""I'm learning about sorting 
    algorithms in Python and would like 
    some guidance on implementation. Can 
    you help me?""",
)

This initiates a chat between the Student and CodingMentor agents with an initial message from the student.

Output

CodingMentor (to chat_manager):

There are many different sorting algorithms available in Python, but I can give you an overview of a few popular ones:

1. Bubble Sort: This is the most straightforward sorting algorithm, and it works by iterating through the array and comparing adjacent elements. If two elements are in the wrong order, they swap places until no more swaps are needed. The algorithm repeats this process until the entire array is sorted.
2. Insertion Sort: This algorithm works by starting at the beginning of the array and comparing each element to its predecessor. If the current element is smaller than the predecessor, it moves one position back in the array until it finds a larger element. Then, it inserts the current element into the correct position.
3. Selection Sort: This algorithm works by selecting the smallest element from the unsorted part of the array and placing it at the beginning of the sorted part. It then repeats this process for the remaining elements in the unsorted part until the array is sorted.
4. Merge Sort: This algorithm works by dividing the array into two halves, sorting each half recursively, and merging the two sorted halves back together. It’s a more efficient algorithm than Bubble Sort or Insertion Sort because it only makes one pass through the array at most.
5. Quick Sort: This algorithm selects a pivot element from the unsorted part of the array, partitions the array around that pivot, and then recursively sorts the two partitions. It’s a more efficient algorithm than Bubble Sort or Selection Sort because it has an average time complexity of O(n log n).

Bubble Sort

Bubble sort is a simple sorting algorithm that repeatedly iterates through the array and compares adjacent elements. If two elements are in the wrong order, they swap places until no more swaps are needed. The algorithm repeats this process until the entire array is sorted.

Here’s an example of how to implement bubble sort in Python:

def bubble_sort(arr):

n = len(arr)

for i in range(n-1):

for j in range(0, n-i-1):

if arr[j] > arr[j+1]:

arr[j], arr[j+1] = arr[j+1], arr[j]

return arr

Insertion Sort

Insertion sort is a simple sorting algorithm that starts at the beginning of the array and compares each element to its predecessor. If the current element is smaller than the predecessor, it moves one position back in the array until it finds a larger element. Then, it inserts the current element into the correct position.

Here’s an example of how to implement insertion sort in Python:

def insertion_sort(arr):

n = len(arr)

for i in range(1, n):

key = arr[i]

j = i-1

while j >= 0 and arr[j] > key:

arr[j+1] = arr[j]

j -= 1

arr[j+1] = key

return arr

Selection Sort

Selection sort is a simple sorting algorithm that works by selecting the smallest element from the unsorted part of the array and placing it at the beginning of the sorted part. It then repeats this process for the remaining elements in the unsorted part until the array is sorted.

Here’s an example of how to implement selection sort in Python:

def selection_sort(arr):

n = len(arr)

for i in range(n-1):

min_idx = i

for j in range(i+1, n):

if arr[j] < arr[min_idx]:

min_idx = j

arr[i], arr[min_idx] = arr[min_idx], arr[i]

return arr

Merge Sort

Merge sort is a more efficient sorting algorithm that works by dividing the array into two halves, sorting each half recursively, and then merging the two sorted halves. It has an average time complexity of O(n log n).

Here’s an example of how to implement merge sort in Python:

def merge_sort(arr):

if len(arr) <= 1:

return arr

mid = len(arr) // 2

left = merge_sort(arr[:mid])

right = merge_sort(arr[mid:])

return merge(left, right)

def merge(left, right):

result = []

while len(left) > 0 and len(right) > 0:

if left[0] <= right[0]:

result.append(left.pop(0))

else:

result.append(right.pop(0))

if len(left) > 0:

result += left

if len(right) > 0:

result += right

return result

Quick Sort

Quick sort is a more efficient sorting algorithm that selects a pivot element from the unsorted part of the array, partitions the array around that pivot, and then recursively sort the two partitions. It has an average time complexity of O(n log n).

Here’s an example of how to implement quick sort in Python:

def quick_sort(arr):

if len(arr) <= 1:

return arr

pivot = arr[0]

less = [x for x in arr[1:] if x <= pivot]

greater = [x for x in arr[1:] if x > pivot]

return quick_sort(less) + [pivot] + quick_sort(greater)

I hope this helps! Let me know if you have any questions or need further clarification on these algorithms.

Provide feedback to chat_manager. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation: can you give real-time usecases

Student (to chat_manager):

can you give real-time usecases

CodingMentor (to chat_manager):

Real-world Use Cases for Sorting Algorithms

Sure, here are some real-world use cases for each sorting algorithm

Bubble Sort

Bubble sort is often used as a simple example to explain the basic concept of a sorting algorithm. It’s easy to understand and implement but inefficient for large datasets. One real-world use case for bubble sort is in a school project management system where you must sort projects based on their due dates or priority levels.

Insertion Sort

Insertion sort is a simple sorting algorithm that’s often used as a stepping stone for more advanced algorithms like quicksort and mergesort. It has a time complexity of O(n^2), which makes it less efficient than some other sorting algorithms for large datasets.One real-world use case for insertion sort is in a social media platform where you need to sort users based on their follower counts or engagement levels.

Selection Sort

Selection sort is another simple sorting algorithm that’s easy to understand and implement, but it’s not very efficient for large datasets. It has a time complexity of O(n^2), which makes it less efficient than some other sorting algorithms.One real-world use case for selection sort is in a library management system where you need to sort books based on their titles or authors.

Merge Sort

Merge sort is a more efficient sorting algorithm with an average time complexity of O(n log n). It’s commonly used in many real-world applications, such as sorting large datasets, searching for specific data, and performing other operations that require efficient sorting. One real-world use case for merge sort is in a search engine where you need to sort web pages based on their relevance or popularity.

Quick Sort

Quick sort is another efficient sorting algorithm with an average time complexity of O(n log n). It’s commonly used in many real-world applications, such as sorting large datasets, searching for specific data, and performing other operations that require efficient sorting. One real-world use case for quick sort is in a social media platform where you need to sort users based on their follower counts or engagement levels.

In general, the choice of which sorting algorithm to use depends on the size of the dataset, the type of data being sorted, and the application’s specific requirements.

AlgorithmExpert (to chat_manager)

Thank you for providing some real-world use cases for each sorting algorithm. This helps to illustrate the different applications and scenarios in which each algorithm can be used.

Additional Thoughts on Each Algorithm

Here are some additional thoughts on each of these algorithms:

• Bubble Sort: One potential drawback of bubble sort is that it’s not as efficient as other sorting algorithms, such as quicksort or mergesort. This means that it may take longer to complete for larger datasets. However, bubble sort is still a valuable algorithm to understand and learn about in the context of computer science and programming. It’s a simple example that helps developers understand the basic concepts of sorting algorithms.
• Insertion Sort: One potential drawback of insertion sort is that it has a time complexity of O(n^2), which means it can be less efficient than other sorting algorithms for large datasets. However, insertion sort is still helpful because it’s an essential building block for more advanced sorting algorithms like quicksort and mergesort.
• Selection Sort: One potential drawback of selection sort is its time complexity of O(n^2), which means it can be less efficient than other sorting algorithms for large datasets. However, selection sort is still a valuable algorithm to learn about because it’s an essential building block for more advanced sorting algorithms like quicksort and mergesort.
• Merge Sort: One potential drawback of merge sort is that it has an average time complexity of O(n log n), which means it’s generally more efficient than other sorting algorithms. However, merge sort can be less efficient for large datasets, and it may not be the best choice for all scenarios.

Provide feedback to chat_manager. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation: exit.

Use Case 2:Automated Financial Stock Price Chart Generation with AutoGen

This AutoGen use case demonstrates the automation of generating a stock price chart for specified companies Amazon(AMZN), Apple(AAPL), and Intel Corporation ( INTC) Year-to-Date (YTD). The assistant agent utilizes the yfinance, matplotlib, and pandas packages to fetch stock prices and create a visually appealing chart. The user proxy agent initiates the conversation with a clear task description, and the assistant responds by generating Python code to accomplish the requested task. The resulting chart is saved as “stock_price_ytd.png.” This use case showcases the seamless integration of AutoGen with external libraries for data retrieval, visualization, and code execution in a real-world scenario.

Coding:

  from autogen import AssistantAgent, UserProxyAgent,oai
config_list = [
    {
        "model": "codellama-7b-instruct.Q5_K_M.gguf",
        "api_base": "http://127.0.0.1:8000/v1",
        "api_type": "open_ai",
        "api_key": "NULL",
    }
]

llm_config = {"config_list": config_list, "seed": 42, "request_timeout": 2028,}

# Create assistant agent
assistant = AssistantAgent(
    name="assistant",
    llm_config=llm_config,
    system_message="""Engineer. You follow an approved plan. 
    You write python/shell code to solve tasks. 
    Wrap the code in a code block that specifies the script type. 
    The user can't modify your code. So do not suggest incomplete 
    code which requires others to modify. Don't use a code block 
    if it's not intended to be executed by the executor.
    Don't include multiple code blocks in one response. Do not ask 
    others to copy and paste the result. Check the execution result 
    returned by the executor. If the result indicates there is 
    an error, fix the error and output the code again. Suggest
    the full code instead of partial code or code changes. If
    the error can't be fixed or if the task is not solved even
    after the code is executed successfully, analyze the 
    problem, revisit your assumption, collect additional info
    you need, and think of a different approach to try.
""",
)

# Create user proxy agent
user_proxy = UserProxyAgent(
    name="user_proxy",
    llm_config=llm_config,
    code_execution_config={"work_dir": "coding"},)

# Start the conversation
user_proxy.initiate_chat(
    assistant,
    message="""Plot a chart of AMZN, AAPL and INTC stock
 price change YTD.use yfinance,matplotlib,pandas packages 
 and save to stock_price_ytd.png.""",)

Output

user_proxy (to assistant):

Plot a chart of AMZN, AAPL and INTC stock
price change YTD.use yfinance,matplotlib,pandas packages
and save to stock_price_ytd.png.

——————————————————————————–
assistant (to user_proxy):
To plot the year-to-date (YTD) stock price changes of Amazon (AMZN), Apple (AAPL), and Intel (INTC) using the yfinance, matplotlib, and pandas packages in Python, you can use the following code:

```python

import yfinance as yf

import matplotlib.pyplot as plt

import pandas as pd

# Get the stock prices for AMZN, AAPL, and INTC
amzn = yf.download('AMZN', start='2018-01-01')['Close']

aapl = yf.download('AAPL', start='2018-01-01')['Close']

intc = yf.download('INTC', start='2018-01-01')['Close']

# Convert the stock prices to a pandas DataFrame
data = pd.DataFrame({'AMZN': amzn, 'AAPL': aapl, 'INTC': intc})

# Plot the YTD price changes using matplotlib
plt.plot(data.index, data['AMZN'], label='AMZN')
plt.plot(data.index, data['AAPL'], label='AAPL')
plt.plot(data.index, data['INTC'], label='INTC')
plt.legend()
plt.xlabel('Date')
plt.ylabel('Price Change (%)')
plt.title('YTD Stock Price Changes')
plt.savefig('stock_price_ytd.png', dpi=300)
```

This code first imports the necessary packages and then downloads the stock prices for AMZN, AAPL, and INTC using the yfinance package. The downloaded data is then converted to a pandas DataFrame and plotted using matplotlib. The YTD price changes are shown as a line graph with the date on the x-axis and the price change in percentage on the y-axis. The legend is displayed to indicate which stock is being represented by each line. Finally, the plot is saved as an image file called `stock_price_ytd.png` using the `savefig()` method of matplotlib.

Note that this code assumes that you have already installed the necessary packages and have a working internet connection. Also, the data used in this example is for demonstration purposes only and may not reflect the actual price changes of these stocks over time.

——————————————————————————–

>>>>>>>> NO HUMAN INPUT RECEIVED.

>>>>>>>> USING AUTO REPLY…

>>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is python)…

WARNING:autogen.code_utils:execute_code was called without specifying a value for use_docker. Since the python docker package is not available, code will be run natively. Note: this fallback behavior is subject to change

user_proxy (to assistant)

exitcode: 0 (execution succeeded)
Code output:

[*********************100%%**********************] 1 of 1 completed

——————————————————————————–

assistant (to user_proxy)

It looks like the code has finished executing successfully. The output shows that it has completed all three tasks, with a success message for each task. The output also shows that each task took approximately 100% of the CPU time and memory available to the process. This suggests that the tasks were executed quickly and efficiently.

If you have any concerns about the output or the code, you may want to check the documentation for the specific libraries and tools being used in the code. Additionally, you can try running the code again with different inputs or parameters to see if there are any differences in the output or execution time.

——————————————————————————–

Provide feedback to assistant. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation: exit

Let’s display the generated figure.

from IPython.display import Image

Image(filename='coding/stock_price_ytd.png')

Note: After running this code successfully, you can find the “stock_price_ytd.png” file in the same directory where your Python script or Jupyter Notebook is located. This is the default behavior of the savefig function from the matplotlib library. If you want to save the image in a different directory, you can provide the full path in the savefig function.

Switching to an IPython Code Execution Environment

Previously, the code was executed in a distinct process, preventing the immediate display of the plot within the notebook. To address this, we can modify the code execution environment to use IPython.

Certainly! Below, we illustrate the process of extending the UserProxyAgent to incorporate a different code execution environment, specifically IPython.

1. Import Packages

from typing import Dict, Union
from IPython import get_ipython
import autogen

2. Class Definition:

class IPythonUserProxyAgent(autogen.UserProxyAgent):

This class inherits from autogen.UserProxyAgent, suggesting it is an extension or customization of a base class.
3. generate_init_message:

def generate_init_message(self, *args, **kwargs) -> Union[str, Dict]:
    return super().generate_init_message(*args, **kwargs) + """
If you suggest code, the code will be executed in IPython."""

This method generates an initial message to send to the assistant agent. It appends a note indicating that code suggestions will be executed in the IPython environment.
4.run_code:

def run_code(self, code, **kwargs):
    result = self._ipython.run_cell("%%capture --no-display cap\n" + code)
    log = self._ipython.ev("cap.stdout")
    log += self._ipython.ev("cap.stderr")
    if result.result is not None:
        log += str(result.result)
    exitcode = 0 if result.success else 1
    if result.error_before_exec is not None:
        log += f"\n{result.error_before_exec}"
        exitcode = 1
    if result.error_in_exec is not None:
        log += f"\n{result.error_in_exec}"
        exitcode = 1
    return exitcode, log, None

This method runs the provided code within the IPython environment. It uses the run_cell method to execute the code and capture the output. The method constructs a log containing standard output, standard error, and additional information about the execution.

Usage Example

import autogen
ipy_user = IPythonUserProxyAgent(
    "ipython_user_proxy",
    human_input_mode="ALWAYS",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get(
    "content", "").rstrip().endswith(
    "TERMINATE") or x.get("content", "").rstrip().endswith('"TERMINATE".'),
)
# the assistant receives a message from the user, which contains the task description
ipy_user.initiate_chat(
    assistant,
    message="""plot a stacked area chart
     visualizing the yearly high and low 
     prices of Apple (AAPL), Google 
     (GOOGL), Amazon (AMZN), and Microsoft (MSFT)
    use yfinance,matplotlib,pandas packages
    """,
)

This snippet creates an instance of IPythonUserProxyAgent and initiates a chat with the assistant, providing a message that includes a task description plot of a stacked area chart visualizing the yearly high and low prices of    Apple (AAPL), Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT)    use yfinance,matplotlib, pandas packages.

Output

ipython_user_proxy (to assistant)

plot a stacked area chart visualizing the yearly high and low prices of
Apple (AAPL), Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT)
use yfinance,matplotlib,pandas packages

If you suggest a code, the code will be executed in IPython.

——————————————————————————–
assistant (to ipython_user_proxy)

Here is a suggested solution using the `yfinance` and `matplotlib` libraries:

```

import yfinance as yf

import matplotlib.pyplot as plt

import pandas as pd

# Get historical data for the four stocks

stocks = ['AAPL', 'GOOGL', 'AMZN', 'MSFT']

prices = yf.download(stocks, start='2010-01-01', end='2022-05-31')['Close']

# Convert to a DataFrame and drop missing values
df = pd.DataFrame(prices)

df = df.dropna()

# Plot the stacked area chart

plt.style.use('ggplot')

fig, ax = plt.subplots(figsize=(12, 6))
ax.stackplot(df.index, df['AAPL'], df['GOOGL'], df['AMZN'], df['MSFT'], labels=stocks)
ax.set_xlabel('Date')

ax.set_ylabel('Price ($)')

ax.legend()

plt.show()

```

This code uses the `yfinance` library to download historical price data for the four stocks from January 1, 2010, to May 31, 2022. It then converts the prices into a DataFrame and drops any missing values (e.g., if a stock was not traded on a particular day). Finally, it uses `matplotlib` to create a stacked area chart showing each stock’s yearly high and low prices. The resulting plot includes a legend with the names of the four stocks.

Note that this is just one possible solution to the problem, and there may be other ways to approach the task depending on your specific needs and requirements.

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Provide feedback to the assistant. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation:

>>>>>>>> NO HUMAN INPUT RECEIVED.

>>>>>>>> USING AUTO REPLY…

>>>>>>>> EXECUTING CODE BLOCK 0 (inferred language is Python)…

ipython_user_proxy (to assistant):
exitcode: 0 (execution succeeded)

Code output:
[*********************100%%**********************] 4 of 4 completed

——————————————————————————–
assistant (to ipython_user_proxy)

The successful execution reveals your output, completing four tasks with progress displayed via a progress bar. Task one is finished at 0%, task two is in progress (shown at 50% completion), task three is also underway (displayed at 75% completion), and task four is completed at 100%.

——————————————————————————–

Provide feedback to the assistant. Press enter to skip and use auto-reply, or type ‘exit’ to end the conversation: exit.

Conclusion

In our AI journey, we’ve uncovered the magic of crafting personalized teams with llama-cpp-python and the collaborative prowess of AutoGen. Forget the queues and hefty bills – with llama-cpp-python. Enter AutoGen – your AI companion for coding quests. Whether you’re building algorithm dream teams or automating financial charts, AutoGen is the hero that makes coding cool. Leverage llama-cpp-python and AutoGen for a personalized AI adventure that unfolds into a masterpiece.

Key Takeaways

• Personalized AI Team Building: The blog introduces the concept of building a personalized AI team without relying on external services like OpenAI and LM Studio.
• Library and Tool Comparison: Different libraries and tools for text completion and language model.
• Web Server and Local LLMs: Explore Llama-cpp-python’s web server functionality, run Local LLMs, and consider hardware acceleration in this examination.
• IPython Code Execution Environment: Integrating IPython as a code execution environment within AutoGen enhances the user experience by enabling immediate display of code execution results.
• Use Cases Showcase Practical Application: Two real-world use cases demonstrate the practical application of AutoGen in scenarios such as building an algorithm mentor team and automating financial stock price chart generation. The examples showcase how AutoGen facilitates seamless user interactions and AI agents to accomplish specific coding tasks.

Frequently Asked Questions

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