The way to Use AutoGen With out Counting on OpenAI or LM Studio? #Imaginations Hub

The way to Use AutoGen With out Counting on OpenAI or LM Studio? #Imaginations Hub
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Introduction

Are you able to create your AI staff with out counting on OpenAI and LM studio? No extra breaking the financial institution or downloading apps. From establishing llama-cpp-python to exploring the facility of native LLMs with the assistance of autogen framework. Get able to unlock the total potential of Autogen with out relying on OpenAI API.

Studying Targets

Earlier than we dive into the main points, let’s define the important thing studying goals of this text:

  • Discover ways to consider and examine totally different AI libraries and instruments.
  • Discover how llama-cpp-python serves as a drop-in substitute for the OpenAI API.
  • Apply the acquired data in two real-world use instances: Constructing an algorithm mentor staff and Automating monetary chart era.
  • Discover AutoGen’s improved person expertise with instantaneous code execution outcomes by means of built-in IPython.

This text was revealed as part of the Information Science Blogathon.

However what’s so particular about this tech toolkit, you ask? Llama-cpp-python is your gateway to working LLMs regionally, together with large names like LLaMA. It’s like having AI superstars proper in your laptop, and with assist for various BLAS backends, the velocity is out of this world!

AutoGen

AutoGen is a unified multi-agent dialog framework that acts as a high-level abstraction for utilizing basis fashions. It combines succesful, customizable, and conversable brokers that combine LLMs, instruments, and human contributors through automated chat. It permits brokers to speak and work collectively autonomously, successfully streamlining advanced duties and automating workflows.

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For those who’re desperate to delve deeper into the capabilities of AutoGen and discover the way it facilitates strategic AI staff constructing, look no additional than our devoted weblog: “Strategic  AI Crew Constructing Made Simple with AutoGen.” This complete useful resource gives insights, use instances, and a more in-depth have a look at how AutoGen can rework your strategy to AI growth.

Some libraries/instruments provide an internet server that goals to behave as a substitute for the OpenAI API.

Library/Instrument Description Language Offers Net Server Acts as OpenAI API Alternative
llama-cpp-python It’s a Python binding for the llama.cpp library gives a high-level Python API for textual content completion with an OpenAI-like API. Python/C++ Sure Sure
oobabooga It’s a Gradio net UI for Giant Language Fashions. It helps transformers, GPTQ, AWQ, EXL2, and llama.cpp (GGUF), Llama fashions Python Sure Sure
LM studio LM Studio is a user-friendly interface that lets you run Giant Language Fashions (LLMs) in your laptop computer offline. Sure Sure
fastchat It’s an open-source library for coaching, serving, and evaluating LLM chat methods Python Sure Sure

Different choices can be found in addition to these above, however one of the best is dependent upon your preferences and desires.

Llama-cpp-python

Llama-cpp-python is a Python binding for the llama.cpp library. It gives low-level entry to the C API through the ctypes interface, high-level Python API for textual content completion, OpenAI-like API, and LangChain compatibility. It helps a number of BLAS backends for quicker processing and likewise helps {hardware} acceleration.

llama-cpp-python affords an internet server that goals to behave as a drop-in substitute for the OpenAI API. This lets you use llama.cpp suitable fashions with any OpenAI-compatible consumer (language libraries, companies, and so on.)

Improvement

For this challenge, we’re going to use these instruments and libraries:

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

Setting Up Your Improvement Surroundings

Create a Digital Surroundings

Digital environments is an efficient observe to isolate project-specific dependencies and keep away from conflicts with system-wide packages. Right here’s the way to arrange a Python setting:

 Venv

python -m venv env_name
  • Activate the Digital Surroundings:
env_nameScriptsactivate
supply env_name/bin/activate

The next command will deactivate the present venv setting:

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 related to the repository must also be cloned. Submodules are separate Git repositories embedded inside the principle repository.
  • https://github.com/abetlen/llama-cpp-python.git: The URL of the Git repository to be cloned.

2. Improve pip

pip set up --upgrade pip

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

%cd llama-cpp-python

4. Modifying Default Token Era Restrict in llama-cpp-python Repository

  1. Clone the “llama-cpp-python” repository to your native machine.
  2. Navigate to the “llama-cpp” folder inside the cloned repository.
  3. Contained in the “server” folder, find the file named “app.py.”
  4. Open “app.py” and scroll all the way down to line 490 or discover the “max_tokens_field”.
  5. Within the “max_tokens_field” part, discover the road that appears like this:
max_tokens_field = Discipline(
    default=16, ge=1, description="The utmost variety of tokens to generate."
)

6.   Change the default worth from 16 to your required worth (e.g., 4096). For instance:

max_tokens_field = Discipline(
    default=4096, ge=1, description="The utmost variety of tokens to generate."
)

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

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

5. Putting in Dependencies

In Google Colab, you usually work in a cloud-based setting with entry to NVIDIA GPUs. Subsequently, for optimum efficiency, think about using a BLAS library that’s suitable with GPU acceleration. Within the case of Google Colab, cuBLAS is an appropriate alternative, as it’s optimized for NVIDIA GPUs, and Colab cases usually include NVIDIA GPU assist.

!CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip set up llama-cpp-python[server]
  • CMAKE_ARGS=”-DLLAMA_CUBLAS=on”: Units the CMake (Cross-Platform Make) configuration argument to allow CUBLAS, a library for GPU-accelerated linear algebra operations.
  • FORCE_CMAKE=1: Forces the CMake configuration to make sure correct setup.
  • pip set up llama-cpp-python[server]: Installs the llama-cpp-python bundle together with extra dependencies specified for server performance.

6. Putting in an Editable bundle

cd llama-cpp-python
!pip set up -e .
!pip set up -e .[server]
  • cd llama-cpp-python: Adjustments the present working listing to llama-cpp-python.
  • pip set up -e .: Installs the present listing as an editable bundle, permitting dynamic modifications to the code with out reinstalling.
  • pip set up -e .[server]: Much like the earlier command, however consists of extra dependencies specified within the [server] extras.

7. Change Listing to ‘llama_cpp/’

cd llama_cpp/Introduction
  •   cd llama_cpp/: Adjustments the present working listing to the llama_cpp subdirectory inside the challenge.

8. Obtain a LLM mannequin

!wget https://huggingface.co/TheBloke/
CodeLlama-7B-Instruct-GGUF/resolve/major/codellama-7b-instruct.Q5_K_M.gguf
  • wget: A command-line utility for downloading recordsdata from the online.

9. Run the Server within the Background

  • Batch Measurement (n_batch): The batch measurement is a hyperparameter that determines the variety of coaching samples to work by means of earlier than the mannequin’s inside parameters are up to date.
  • GPU Layers (n_gpu_layers): The variety of layers to dump to the GPU for computations is normally set primarily based in your VRAM capability. You possibly can improve the quantity step by step till you discover a candy spot. This might help optimize your mannequin’s efficiency by leveraging the GPU’s computational energy.
  • Context Measurement (n_ctx): The context measurement represents the utmost variety of tokens the enter sequence will be. Most fashions are educated with a context measurement of 2048. If you might want to course of an extended immediate, you’ll be able to change it. Nevertheless, reviewing a mannequin’s context restrict is suggested in opposition to because it hasn’t been educated to account for information units bigger than its recommended context restrict.
!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.
  • –mannequin codellama-7b-instruct.Q5_K_M.gguf: Specifies the trail to the downloaded mannequin file.
  • –host 127.0.0.1: Units the host IP tackle to 127.0.0.1 (localhost).
  • –n_ctx 2048: Configures the context measurement to 2048.
  • –n_batch 128: Units the batch measurement to 128.
  • –n_gpu_layers 35: Specifies the variety of GPU layers as 35.
  • > server.log 2>&1 &: Redirects normal output and errors to a file named server.log and runs the method within the background.

These instructions collectively arrange the ‘llama-cpp-python’ bundle, configure it with particular choices, set up extra dependencies for server performance, and provoke a server with a pre-trained mannequin. For additional reference, seek advice from official documentation.

Coding

Use Case 1: Construct your Algorithm Mentor Crew

This code snippet demonstrates the creation of brokers utilizing the `autogen` library for a state of affairs the place a pupil seeks steering from a Coding Mentor and an Algorithm Knowledgeable in implementing sorting algorithms in Python.

"

Right here’s a breakdown of the code:

1. Import the autogen module:

import autogen

This imports the mandatory performance from the autogen library.

2. Outline 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 units up a configuration checklist specifying particulars just like the mannequin to make use of, API base URL, sort of API, and API key (which is a placeholder on this case).

3. Create brokers with totally different roles and configurations:

coding_mentor = autogen.AssistantAgent(
    title="CodingMentor",
    llm_config=
        "seed": 42,
        "config_list": config_list,
        "temperature": 0.7,
        "request_timeout": 1200,
    ,
    system_message="Coding Mentor right here! I
     can information you thru implementing 
     sorting algorithms in Python.",
)

This creates an agent named CodingMentor with a selected configuration for the language mannequin (llm_config), together with particulars like seed, mannequin configuration checklist, temperature, and request timeout.

algorithm_expert = autogen.AssistantAgent(
    title="AlgorithmExpert",
    llm_config=
        "seed": 42,
        "config_list": config_list,
        "temperature": 0.7,
        "request_timeout": 1200,
    ,
    system_message="Algorithm Knowledgeable. I 
    specialise in algorithms. Let's work 
    on implementing a sorting algorithm 
    collectively.",
)

Equally, this creates one other agent named AlgorithmExpert with comparable configurations.

pupil = autogen.UserProxyAgent(
    title="Scholar",
    human_input_mode="ALWAYS",
    code_execution_config="work_dir":"node",
)

This creates a person proxy agent named Scholar with the human_input_mode set to “ALWAYS,” indicating that the agent will at all times count on human enter.

4. Provoke a chat between the coed and the Coding Mentor:

pupil.initiate_chat(
    coding_mentor,
    message="""I am studying about sorting 
    algorithms in Python and would love 
    some steering on implementation. Can 
    you assist me?""",
)

This initiates a chat between the Scholar and CodingMentor brokers with an preliminary message from the coed.

Output

CodingMentor (to chat_manager):

There are various totally different sorting algorithms out there in Python, however I may give you an outline of some fashionable ones:

  1. Bubble Type: That is essentially the most easy sorting algorithm, and it really works by iterating by means of the array and evaluating adjoining components. If two components are within the mistaken order, they swap locations till no extra swaps are wanted. The algorithm repeats this course of till the whole array is sorted.
  2. Insertion Type: This algorithm works by beginning in the beginning of the array and evaluating every aspect to its predecessor. If the present aspect is smaller than the predecessor, it strikes one place again within the array till it finds a bigger aspect. Then, it inserts the present aspect into the right place.
  3. Choice Type: This algorithm works by deciding on the smallest aspect from the unsorted a part of the array and inserting it in the beginning of the sorted half. It then repeats this course of for the remaining components within the unsorted half till the array is sorted.
  4. Merge Type: This algorithm works by dividing the array into two halves, sorting every half recursively, and merging the 2 sorted halves again collectively. It’s a extra environment friendly algorithm than Bubble Type or Insertion Type as a result of it solely makes one go by means of the array at most.
  5. Fast Type: This algorithm selects a pivot aspect from the unsorted a part of the array, partitions the array round that pivot, after which recursively kinds the 2 partitions. It’s a extra environment friendly algorithm than Bubble Type or Choice Type as a result of it has a median time complexity of O(n log n).

Bubble Type

Bubble kind is a straightforward sorting algorithm that repeatedly iterates by means of the array and compares adjoining components. If two components are within the mistaken order, they swap locations till no extra swaps are wanted. The algorithm repeats this course of till the whole array is sorted.

Right here’s an instance of the way to implement bubble kind in Python:

def bubble_sort(arr):

n = len(arr)

for i in vary(n-1):

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

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

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

return arr

Insertion Type

Insertion kind is a straightforward sorting algorithm that begins in the beginning of the array and compares every aspect to its predecessor. If the present aspect is smaller than the predecessor, it strikes one place again within the array till it finds a bigger aspect. Then, it inserts the present aspect into the right place.

Right here’s an instance of the way to implement insertion kind in Python:

def insertion_sort(arr):

n = len(arr)

for i in vary(1, n):

key = arr[i]

j = i-1

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

arr[j+1] = arr[j]

j -= 1

arr[j+1] = key

return arr

Choice Type

Choice kind is a straightforward sorting algorithm that works by deciding on the smallest aspect from the unsorted a part of the array and inserting it in the beginning of the sorted half. It then repeats this course of for the remaining components within the unsorted half till the array is sorted.

Right here’s an instance of the way to implement choice kind in Python:

def selection_sort(arr):

n = len(arr)

for i in vary(n-1):

min_idx = i

for j in vary(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 Type

Merge kind is a extra environment friendly sorting algorithm that works by dividing the array into two halves, sorting every half recursively, after which merging the 2 sorted halves. It has a median time complexity of O(n log n).

Right here’s an instance of the way to implement merge kind in Python:

def merge_sort(arr):

if len(arr) <= 1:

return arr

mid = len(arr) // 2

left = merge_sort(arr[:mid])

proper = merge_sort(arr[mid:])

return merge(left, proper)

def merge(left, proper):

outcome = []

whereas len(left) > 0 and len(proper) > 0:

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

outcome.append(left.pop(0))

else:

outcome.append(proper.pop(0))

if len(left) > 0:

outcome += left

if len(proper) > 0:

outcome += proper

return outcome

Fast Type

Fast kind is a extra environment friendly sorting algorithm that selects a pivot aspect from the unsorted a part of the array, partitions the array round that pivot, after which recursively kind the 2 partitions. It has a median time complexity of O(n log n).

Right here’s an instance of the way to implement fast kind in Python:

def quick_sort(arr):

if len(arr) <= 1:

return arr

pivot = arr[0]

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

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

return quick_sort(much less) + [pivot] + quick_sort(larger)

I hope this helps! Let me know when you have any questions or want additional clarification on these algorithms.

Present suggestions to chat_manager. Press enter to skip and use auto-reply, or sort ‘exit’ to finish the dialog: are you able to give real-time usecases

Scholar (to chat_manager):

are you able to give real-time usecases

CodingMentor (to chat_manager):

Actual-world Use Circumstances for Sorting Algorithms

Certain, listed below are some real-world use instances for every sorting algorithm

Bubble Type

Bubble kind is commonly used as a easy instance to clarify the essential idea of a sorting algorithm. It’s straightforward to grasp and implement however inefficient for big datasets. One real-world use case for bubble kind is in a faculty challenge administration system the place you should kind tasks primarily based on their due dates or precedence ranges.

Insertion Type

Insertion kind is a straightforward sorting algorithm that’s usually used as a stepping stone for extra superior algorithms like quicksort and mergesort. It has a time complexity of O(n^2), which makes it much less environment friendly than another sorting algorithms for big datasets.One real-world use case for insertion kind is in a social media platform the place you might want to kind customers primarily based on their follower counts or engagement ranges.

Choice Type

Choice kind is one other easy sorting algorithm that’s straightforward to grasp and implement, nevertheless it’s not very environment friendly for big datasets. It has a time complexity of O(n^2), which makes it much less environment friendly than another sorting algorithms.One real-world use case for choice kind is in a library administration system the place you might want to kind books primarily based on their titles or authors.

Merge Type

Merge kind is a extra environment friendly sorting algorithm with a median time complexity of O(n log n). It’s generally utilized in many real-world purposes, equivalent to sorting massive datasets, looking for particular information, and performing different operations that require environment friendly sorting. One real-world use case for merge kind is in a search engine the place you might want to kind net pages primarily based on their relevance or recognition.

Fast Type

Fast kind is one other environment friendly sorting algorithm with a median time complexity of O(n log n). It’s generally utilized in many real-world purposes, equivalent to sorting massive datasets, looking for particular information, and performing different operations that require environment friendly sorting. One real-world use case for fast kind is in a social media platform the place you might want to kind customers primarily based on their follower counts or engagement ranges.

Basically, the selection of which sorting algorithm to make use of is dependent upon the scale of the dataset, the kind of information being sorted, and the appliance’s particular necessities.

AlgorithmExpert (to chat_manager)

Thanks for offering some real-world use instances for every sorting algorithm. This helps as an example the totally different purposes and eventualities wherein every algorithm can be utilized.

Further Ideas on Every Algorithm

Listed below are some extra ideas on every of those algorithms:

  • Bubble Type: One potential disadvantage of bubble kind is that it’s not as environment friendly as different sorting algorithms, equivalent to quicksort or mergesort. Because of this it could take longer to finish for bigger datasets. Nevertheless, bubble kind continues to be a precious algorithm to grasp and study within the context of laptop science and programming. It’s a easy instance that helps builders perceive the essential ideas of sorting algorithms.
  • Insertion Type: One potential disadvantage of insertion kind is that it has a time complexity of O(n^2), which suggests it may be much less environment friendly than different sorting algorithms for big datasets. Nevertheless, insertion kind continues to be useful as a result of it’s an important constructing block for extra superior sorting algorithms like quicksort and mergesort.
  • Choice Type: One potential disadvantage of choice kind is its time complexity of O(n^2), which suggests it may be much less environment friendly than different sorting algorithms for big datasets. Nevertheless, choice kind continues to be a precious algorithm to study as a result of it’s an important constructing block for extra superior sorting algorithms like quicksort and mergesort.
  • Merge Type: One potential disadvantage of merge kind is that it has a median time complexity of O(n log n), which suggests it’s typically extra environment friendly than different sorting algorithms. Nevertheless, merge kind will be much less environment friendly for big datasets, and it will not be your best option for all eventualities.

Present suggestions to chat_manager. Press enter to skip and use auto-reply, or sort ‘exit’ to finish the dialog: exit.

Use Case 2:Automated Monetary Inventory Worth Chart Era with AutoGen

This AutoGen use case demonstrates the automation of producing a inventory worth chart for specified firms Amazon(AMZN), Apple(AAPL), and Intel Company ( INTC) 12 months-to-Date (YTD). The assistant agent makes use of the yfinance, matplotlib, and pandas packages to fetch inventory costs and create a visually interesting chart. The person proxy agent initiates the dialog with a transparent activity description, and the assistant responds by producing Python code to perform the requested activity. The ensuing chart is saved as “stock_price_ytd.png.” This use case showcases the seamless integration of AutoGen with exterior libraries for information retrieval, visualization, and code execution in a real-world state of affairs.

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(
    title="assistant",
    llm_config=llm_config,
    system_message="""Engineer. You comply with an authorized plan. 
    You write python/shell code to resolve duties. 
    Wrap the code in a code block that specifies the script sort. 
    The person cannot modify your code. So don't recommend incomplete 
    code which requires others to change. Do not use a code block 
    if it isn't supposed to be executed by the executor.
    Do not embody a number of code blocks in a single response. Don't ask 
    others to repeat and paste the outcome. Examine the execution outcome 
    returned by the executor. If the outcome signifies there's 
    an error, repair the error and output the code once more. Recommend
    the total code as an alternative of partial code or code modifications. If
    the error cannot be fastened or if the duty shouldn't be solved even
    after the code is executed efficiently, analyze the 
    drawback, revisit your assumption, accumulate more information
    you want, and consider a special strategy to attempt.
""",
)

# Create person proxy agent
user_proxy = UserProxyAgent(
    title="user_proxy",
    llm_config=llm_config,
    code_execution_config="work_dir": "coding",)

# Begin the dialog
user_proxy.initiate_chat(
    assistant,
    message="""Plot a chart of AMZN, AAPL and INTC inventory
 worth 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 inventory
worth 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) inventory worth modifications of Amazon (AMZN), Apple (AAPL), and Intel (INTC) utilizing the yfinance, matplotlib, and pandas packages in Python, you should use the next code:

```python

import yfinance as yf

import matplotlib.pyplot as plt

import pandas as pd

# Get the inventory costs for AMZN, AAPL, and INTC
amzn = yf.obtain('AMZN', begin="2018-01-01")['Close']

aapl = yf.obtain('AAPL', begin="2018-01-01")['Close']

intc = yf.obtain('INTC', begin="2018-01-01")['Close']

# Convert the inventory costs to a pandas DataFrame
information = pd.DataFrame('AMZN': amzn, 'AAPL': aapl, 'INTC': intc)

# Plot the YTD worth modifications utilizing matplotlib
plt.plot(information.index, information['AMZN'], label="AMZN")
plt.plot(information.index, information['AAPL'], label="AAPL")
plt.plot(information.index, information['INTC'], label="INTC")
plt.legend()
plt.xlabel('Date')
plt.ylabel('Worth Change (%)')
plt.title('YTD Inventory Worth Adjustments')
plt.savefig('stock_price_ytd.png', dpi=300)
```

This code first imports the mandatory packages after which downloads the inventory costs for AMZN, AAPL, and INTC utilizing the yfinance bundle. The downloaded information is then transformed to a pandas DataFrame and plotted utilizing matplotlib. The YTD worth modifications are proven as a line graph with the date on the x-axis and the value change in proportion on the y-axis. The legend is displayed to point which inventory is being represented by every line. Lastly, the plot is saved as a picture file referred to as `stock_price_ytd.png` utilizing the `savefig()` methodology of matplotlib.

Notice that this code assumes that you’ve already put in the mandatory packages and have a working web connection. Additionally, the info used on this instance is for demonstration functions solely and should not replicate the precise worth modifications of those shares over time.

——————————————————————————–

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

>>>>>>>> USING AUTO REPLY…

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

WARNING:autogen.code_utils:execute_code was referred to as with out specifying a price for use_docker. Because the python docker bundle shouldn’t be out there, code might be run natively. Notice: this fallback habits is topic to alter

user_proxy (to assistant)

exitcode: 0 (execution succeeded)
Code output:

[*********************100%%**********************] 1 of 1 accomplished

——————————————————————————–

assistant (to user_proxy)

It appears to be like just like the code has completed executing efficiently. The output exhibits that it has accomplished all three duties, with a hit message for every activity. The output additionally exhibits that every activity took roughly 100% of the CPU time and reminiscence out there to the method. This means that the duties had been executed rapidly and effectively.

You probably have any issues in regards to the output or the code, it’s possible you’ll wish to test the documentation for the particular libraries and instruments getting used within the code. Moreover, you’ll be able to attempt working the code once more with totally different inputs or parameters to see if there are any variations within the output or execution time.

——————————————————————————–

Present suggestions to assistant. Press enter to skip and use auto-reply, or sort ‘exit’ to finish the dialog: exit

Let’s show the generated determine.

from IPython.show import Picture

Picture(filename="coding/stock_price_ytd.png")
"

Notice: After working this code efficiently, you’ll find the “stock_price_ytd.png” file in the identical listing the place your Python script or Jupyter Pocket book is positioned. That is the default habits of the savefig operate from the matplotlib library. If you wish to save the picture in a special listing, you’ll be able to present the total path within the savefig operate.

"

Switching to an IPython Code Execution Surroundings

Beforehand, the code was executed in a definite course of, stopping the rapid show of the plot inside the pocket book. To deal with this, we will modify the code execution setting to make use of IPython.

Definitely! Under, we illustrate the method of extending the UserProxyAgent to include a special code execution setting, particularly 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’s an extension or customization of a base class.
3. generate_init_message:

def generate_init_message(self, *args, **kwargs) -> Union[str, Dict]:
    return tremendous().generate_init_message(*args, **kwargs) + """
For those who recommend code, the code might be executed in IPython."""

This methodology generates an preliminary message to ship to the assistant agent. It appends a notice indicating that code strategies might be executed within the IPython setting.
4.run_code:

def run_code(self, code, **kwargs):
    outcome = self._ipython.run_cell("%%seize --no-display capn" + code)
    log = self._ipython.ev("cap.stdout")
    log += self._ipython.ev("cap.stderr")
    if outcome.outcome shouldn't be None:
        log += str(outcome.outcome)
    exitcode = 0 if outcome.success else 1
    if outcome.error_before_exec shouldn't be None:
        log += f"nresult.error_before_exec"
        exitcode = 1
    if outcome.error_in_exec shouldn't be None:
        log += f"nresult.error_in_exec"
        exitcode = 1
    return exitcode, log, None

This methodology runs the offered code inside the IPython setting. It makes use of the run_cell methodology to execute the code and seize the output. The tactic constructs a log containing normal output, normal error, and extra details about the execution.

Utilization Instance

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 material", "").rstrip().endswith(
    "TERMINATE") or x.get("content material", "").rstrip().endswith('"TERMINATE".'),
)
# the assistant receives a message from the person, which incorporates the duty description
ipy_user.initiate_chat(
    assistant,
    message="""plot a stacked space chart
     visualizing the yearly excessive and low 
     costs of Apple (AAPL), Google 
     (GOOGL), Amazon (AMZN), and Microsoft (MSFT)
    use yfinance,matplotlib,pandas packages
    """,
)

This snippet creates an occasion of IPythonUserProxyAgent and initiates a chat with the assistant, offering a message that features a activity description plot of a stacked space chart visualizing the yearly excessive and low costs of    Apple (AAPL), Google (GOOGL), Amazon (AMZN), and Microsoft (MSFT)    use yfinance,matplotlib, pandas packages.

Output

ipython_user_proxy (to assistant)

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

For those who recommend a code, the code might be executed in IPython.

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

Here’s a recommended answer utilizing the `yfinance` and `matplotlib` libraries:

```

import yfinance as yf

import matplotlib.pyplot as plt

import pandas as pd

# Get historic information for the 4 shares

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

costs = yf.obtain(shares, begin="2010-01-01", finish='2022-05-31')['Close']

# Convert to a DataFrame and drop lacking values
df = pd.DataFrame(costs)

df = df.dropna()

# Plot the stacked space chart

plt.type.use('ggplot')

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

ax.set_ylabel('Worth ($)')

ax.legend()

plt.present()

```

This code makes use of the `yfinance` library to obtain historic worth information for the 4 shares from January 1, 2010, to Could 31, 2022. It then converts the costs right into a DataFrame and drops any lacking values (e.g., if a inventory was not traded on a selected day). Lastly, it makes use of `matplotlib` to create a stacked space chart displaying every inventory’s yearly excessive and low costs. The ensuing plot features a legend with the names of the 4 shares.

Notice that this is only one doable answer to the issue, and there could also be different methods to strategy the duty relying in your particular wants and necessities.

——————————————————————————–
Present suggestions to the assistant. Press enter to skip and use auto-reply, or sort ‘exit’ to finish the dialog:

>>>>>>>> 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 accomplished

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

The profitable execution reveals your output, finishing 4 duties with progress displayed through a progress bar. Job one is completed at 0%, activity two is in progress (proven at 50% completion), activity three can also be underway (displayed at 75% completion), and activity 4 is accomplished at 100%.

——————————————————————————–

Present suggestions to the assistant. Press enter to skip and use auto-reply, or sort ‘exit’ to finish the dialog: exit.

Conclusion

In our AI journey, we’ve uncovered the magic of crafting personalised groups with llama-cpp-python and the collaborative prowess of AutoGen. Overlook the queues and hefty payments – with llama-cpp-python. Enter AutoGen – your AI companion for coding quests. Whether or not you’re constructing algorithm dream groups or automating monetary charts, AutoGen is the hero that makes coding cool. Leverage llama-cpp-python and AutoGen for a personalised AI journey that unfolds right into a masterpiece.

Key Takeaways

  • Customized AI Crew Constructing: The weblog introduces the idea of constructing a personalised AI staff with out counting on exterior companies like OpenAI and LM Studio.
  • Library and Instrument Comparability: Completely different libraries and instruments for textual content completion and language mannequin.
  • Net Server and Native LLMs: Discover Llama-cpp-python’s net server performance, run Native LLMs, and take into account {hardware} acceleration on this examination.
  • IPython Code Execution Surroundings: Integrating IPython as a code execution setting inside AutoGen enhances the person expertise by enabling rapid show of code execution outcomes.
  • Use Circumstances Showcase Sensible Software: Two real-world use instances reveal the sensible utility of AutoGen in eventualities equivalent to constructing an algorithm mentor staff and automating monetary inventory worth chart era. The examples showcase how AutoGen facilitates seamless person interactions and AI brokers to perform particular coding duties.

Often Requested Questions

Q1. What’s llama-cpp-python, and the way does it differ from different language mannequin interplay instruments?

A. llama-cpp-python is a Python binding for the llama.cpp library, offering each low-level and high-level entry to language mannequin APIs. Its key distinction lies in performing as a neighborhood OpenAI API substitute, lowering prices and enhancing privateness.

Q2. What issues ought to be made when selecting a BLAS library for llama-cpp-python?

A. Elements equivalent to {hardware} compatibility, programming setting, efficiency necessities, and ease of integration affect the selection of BLAS library.

Q3. What’s the significance of the seed in AutoGen, and the way does it guarantee reproducibility in code era?

A. The seed in AutoGen ensures reproducibility in code era. Setting the seed to a selected numeric worth generates constant outcomes throughout duties.

This fall. Can AutoGen’s collaborative coding setting be prolonged to make use of totally different code execution environments?

A. Sure, AutoGen’s collaborative coding setting will be prolonged. This weblog demonstrates switching to an IPython code execution setting to deal with show points and guarantee rapid plot visibility inside the pocket book.

The media proven on this article shouldn’t be owned by Analytics Vidhya and is used on the Creator’s discretion.


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