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SETUP.md

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To structure and detail the setup for a working directory containing an __init__.py file and various AutoGen scripts, I'll break down the process into two parts. In this response, I'll focus on setting up the working directory and initializing it for a Python project, particularly with an __init__.py file and the basics of AutoGen scripts integration.

Part 1: Setting Up the Working Directory and __init__.py File

1. Creating the Working Directory

Start by creating a dedicated directory for your project. This can be done using the command line or through a file explorer.

mkdir my_project
cd my_project

2. Initializing the Directory as a Python Package

Inside this directory, you'll need an __init__.py file. This file can be empty but it signifies to Python that this directory should be treated as a package.

touch __init__.py

3. Structuring the Directory

Consider the structure of your project. A typical Python project might look like this:

my_project/
│
├── __init__.py
├── main.py
├── autogen/
│   ├── __init__.py
│   ├── agent.py
│   ├── user_proxy.py
│   └── ... (other autogen scripts)
└── utils/
    ├── __init__.py
    └── ... (utility scripts)
  • main.py: The entry point of your application.
  • autogen/: A sub-package containing AutoGen-related scripts.
  • utils/: Another sub-package for utility functions and classes.

4. Writing AutoGen Scripts

In the autogen directory, place your AutoGen scripts like agent.py, user_proxy.py, etc. Each of these scripts should be structured as a Python module.

For example, in agent.py:

# agent.py
class Agent:
    def __init__(self, config):
        # Initialization logic
        pass

    def process(self, input_data):
        # Processing logic
        pass

# Additional functions and classes as needed

Each script should focus on a specific aspect of your application's functionality.

5. Utilizing the __init__.py Files

In each subdirectory's __init__.py, you can control what gets imported when the package is imported. You might not need to write anything in these files initially, but they can be used to simplify imports in your project.

For example, in autogen/__init__.py:

from .agent import Agent
from .user_proxy import UserProxy
# ... other imports

This structure sets a clear and organized foundation for your Python project, enabling efficient development and maintenance.

In the next response, I'll detail how to integrate the AutoGen scripts into your main application logic, focusing on how these components interact and work together within the context of your project.

To initialize the agents like user_proxy, assistant, planner, retriever, and researcher in your Python project using AutoGen, you need to first ensure that these agents are defined properly in their respective scripts under the autogen directory. Let's outline the initialization process for each agent:

1. Assistant Agent

The Assistant Agent can be thought of as the primary conversational AI that interacts with the user.

In assistant.py:

from autogen import AssistantAgent

assistant = AssistantAgent(
    name="assistant_agent",
    llm_config={"config_list": config_list}  # Replace with your actual configuration
)

2. User Proxy Agent

The User Proxy Agent acts as an intermediary between the user and the other agents.

In user_proxy.py:

from autogen import UserProxyAgent

user_proxy = UserProxyAgent(
    name="user_proxy_agent",
    human_input_mode="TERMINATE",  # Or other modes as per your requirement
    max_consecutive_auto_reply=10
)

3. Planner Agent

The Planner Agent is responsible for planning tasks and making decisions based on input.

In planner.py:

from autogen import PlannerAgent

planner = PlannerAgent(
    name="planner_agent",
    llm_config={"config_list": config_list}  # Replace with your actual configuration
)

4. Retriever Agent

The Retriever Agent is designed for fetching data from various sources, like databases or APIs.

In retriever.py:

from autogen import RetrieverAgent

retriever = RetrieverAgent(
    name="retriever_agent",
    llm_config={"config_list": config_list}  # Replace with your actual configuration
)

5. Researcher Agent

The Researcher Agent specializes in conducting research or complex queries.

In researcher.py:

from autogen import ResearcherAgent

researcher = ResearcherAgent(
    name="researcher_agent",
    llm_config={"config_list": config_list}  # Replace with your actual configuration
)

Main Application Integration (main.py)

In your main application file, you can bring all these agents together. Here's a basic setup:

# main.py
from autogen.user_proxy import user_proxy
from autogen.assistant import assistant
from autogen.planner import planner
from autogen.retriever import retriever
from autogen.researcher import researcher

# Example: Start a conversation with the assistant
user_proxy.initiate_chat(assistant, message="Hello, Assistant!")

# Further integration logic goes here

This setup provides a foundational structure for initializing and integrating various agents in your AI application. Each agent can be customized further based on your specific requirements and the functionalities you want to incorporate. Remember to replace placeholders like config_list with your actual configurations.