basic2.py

"""
This script demonstrates a more advanced setup for an AutoGen chat environment,
including a follow-up question and the generation of a chart.

The script does the following:
1. Configures an AssistantAgent with specific LLM settings.
2. Sets up a UserProxyAgent with specific settings for human input and code execution.
3. Initiates a chat with questions about the current date and tech stock performance.
4. Sends a follow-up message to plot and save a chart of stock price changes.

Dependencies:
    - autogen
    - os
    - dotenv
    - openai

Environment Variables:
    - OPENAI_API_KEY: Your OpenAI API key

Output:
    - Initiates a chat session answering questions about stocks.
    - Generates and saves a plot of stock price changes to 'stock_price_ytd.png'.
"""

from autogen import AssistantAgent, UserProxyAgent, config_list_from_json
import os
from dotenv import load_dotenv
import openai

load_dotenv()

# Import the openai api key
config_list = config_list_from_json(env_or_file="OAI_CONFIG_LIST")
openai.api_key = os.getenv("OPENAI_API_KEY")

# Create assistant agent
assistant = AssistantAgent(
    name="assistant",
    llm_config={
        "seed": 42,
        "config_list": config_list,
        "temperature": 0
    }
)

# Create user proxy agent
user_proxy = UserProxyAgent(
    name="user_proxy",
    system_message="A Human input",
    human_input_mode="TERMINATE",
    max_consecutive_auto_reply=10,
    is_termination_msg=lambda x: x.get(
        "content", "").rstrip().endswith("TERMINATE"),
    code_execution_config={
        "work_dir": "coding",
        "use_docker": True
    }
)

# Start the conversation
user_proxy.initiate_chat(
    assistant,
    message="""what date is today? Which big tech stock has the largest year-to-date 
    gain this year? How much is the gain?"""
)

# followup of the previous question
user_proxy.send(
    message="""Plot a chart of their stock price change YTD and save the python file 
    and save the plot to stock_price_ytd.png""",
    recipient=assistant
)