You can also check this cookbook in colab here.

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This notebook demonstrates how to set up and leverage CAMEL’s ability to use BaseMessage class.

In this notebook, you’ll explore:

  • CAMEL: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.

  • BaseMessage: The base class for message objects used in the CAMEL chat system. It is designed to provide a consistent structure for the messages in the system and allow for easy conversion between different message types.

In this tutorial, we will explore the BaseMessage class. The topics covered include:

  1. Introduction to the BaseMessage class.
  2. Creating a BaseMessage instance.
  3. Understanding the properties of the BaseMessage class.
  4. Using the methods of the BaseMessage class.
  5. Give message to ChatAgent

​
📦 Installation

First, install the CAMEL package with all its dependencies:

!pip install "camel-ai==0.2.16"

​
🔑 Setting Up API Keys

You’ll need to set up your API keys for OpenAI. This ensures that the tools can interact with external services securely.

import os
from getpass import getpass

# Prompt for the API key securely
openai_api_key = getpass('Enter your API key: ')
os.environ["OPENAI_API_KEY"] = openai_api_key

You can go to here to get free API Key from AgentOps

import os
from getpass import getpass

# Prompt for the AgentOps API key securely
agentops_api_key = getpass('Enter your API key: ')
os.environ["AGENTOPS_API_KEY"] = agentops_api_key

Your can go to here to get API Key from Mistral AI with free credits.

# Prompt for the API key securely
mistral_api_key = getpass('Enter your API key: ')
os.environ["MISTRAL_API_KEY"] = mistral_api_key

Alternatively, if running on Colab, you could save your API keys and tokens as Colab Secrets,

and use them across notebooks.

To do so, comment out the above manual API key prompt code block(s),

and uncomment the following codeblock.

⚠️ Don’t forget granting access to the API key you would be using to the current notebook.

# import os
# from google.colab import userdata

# os.environ["OPENAI_API_KEY"] = userdata.get("OPENAI_API_KEY")
# os.environ["AGENTOPS_API_KEY"] = userdata.get("AGENTOPS_API_KEY")
# os.environ["MISTRAL_API_KEY"] = userdata.get("MISTRAL_API_KEY")

​
Give message to ChatAgent directly

You can simply pass text message to ChatAgent

from camel.agents import ChatAgent

# Define system message
sys_msg = "You are a helpful assistant."

# Set agent
camel_agent = ChatAgent(system_message=sys_msg)

# Set user message
user_msg = """Say hi to CAMEL AI, one open-source community dedicated to the
    study of autonomous and communicative agents."""

# Get response information
response = camel_agent.step(user_msg)
print(response.msgs[0].content)

​
Give message to ChatAgent via BaseMessage

For more complex message usage like multi-modal message, we suggest using BaseMessage

​
Creating a BaseMessage Instance

To create a BaseMessage instance, you need to provide the following arguments:

  • role_name: The name of the user or assistant role.
  • role_type: The type of role, either RoleType.ASSISTANT or RoleType.USER.
  • content: The content of the message.
  • meta_dict: An metadata dictionary for the message.

Below are optional arguments you can pass:

  • video_bytes: Optional bytes of a video associated with the message.
  • image_list: Optional list of PIL Image objects associated with the message.
  • image_detail: Detail level of the images associated with the message. Default is “auto”.
  • video_detail: Detail level of the videos associated with the message. Default is “low”.

Here’s an example of creating a BaseMessage instance:

from camel.messages import BaseMessage
from camel.types import RoleType

message = BaseMessage(
    role_name="test_user",
    role_type=RoleType.USER,
    content="test content",
    meta_dict={}
)

Additionally, the BaseMessage class provides class methods to easily create user and assistant agent messages:

  1. Creating a user agent message:
from camel.messages import BaseMessage

user_message = BaseMessage.make_user_message(
    role_name="user_name",
    content="test content for user",
)
  1. Creating an assistant agent message:
from camel.messages import BaseMessage

assistant_message = BaseMessage.make_assistant_message(
    role_name="assistant_name",
    content="test content for assistant",
)

​
Using the Methods of the BaseMessage Class

The BaseMessage class offers several methods:

  1. Creating a new instance with updated content:
new_message = message.create_new_instance("new test content")
print(isinstance(new_message, BaseMessage))
  1. Converting to an OpenAIMessage object:
from camel.types import OpenAIBackendRole
openai_message = message.to_openai_message(role_at_backend=OpenAIBackendRole.USER)
print(openai_message == {"role": "user", "content": "test content"})
  1. Converting to an OpenAISystemMessage object:
openai_system_message = message.to_openai_system_message()
print(openai_system_message == {"role": "system", "content": "test content"})
  1. Converting to an OpenAIUserMessage object:
openai_user_message = message.to_openai_user_message()
print(openai_user_message == {"role": "user", "content": "test content"})
  1. Converting to an OpenAIAssistantMessage object:
openai_assistant_message = message.to_openai_assistant_message()
print(openai_assistant_message == {"role": "assistant", "content": "test content"})
  1. Converting to a dictionary:
message_dict = message.to_dict()
print(message_dict == {
    "role_name": "test_user",
    "role_type": "USER",
    "content": "test content"
})

These methods allow you to convert a BaseMessage instance into different message types depending on your needs.

​
Give BaseMessage to ChatAgent

from io import BytesIO

import requests
from PIL import Image

from camel.agents import ChatAgent
from camel.messages import BaseMessage
# URL of the image
url = "https://raw.githubusercontent.com/camel-ai/camel/master/misc/logo_light.png"
response = requests.get(url)
img = Image.open(BytesIO(response.content))

# Define system message
sys_msg = BaseMessage.make_assistant_message(
    role_name="Assistant",
    content="You are a helpful assistant.",
)

# Set agent
camel_agent = ChatAgent(system_message=sys_msg)

# Set user message
user_msg = BaseMessage.make_user_message(
    role_name="User", content="""what's in the image?""", image_list=[img]
)

# Get response information
response = camel_agent.step(user_msg)
print(response.msgs[0].content)

​
🌟 Highlights

This notebook has guided you through setting up and converting BaseMessage to different types of messages. These components play essential roles in the CAMEL chat system, facilitating the creation, management, and interpretation of messages with clarity.

Key tools utilized in this notebook include:

  • CAMEL: A powerful multi-agent framework that enables Retrieval-Augmented Generation and multi-agent role-playing scenarios, allowing for sophisticated AI-driven tasks.
  • BaseMessage: The base class for message objects used in the CAMEL chat system. It is designed to provide a consistent structure for the messages in the system and allow for easy conversion between different message types.

That’s everything: Got questions about 🐫 CAMEL-AI? Join us on Discord! Whether you want to share feedback, explore the latest in multi-agent systems, get support, or connect with others on exciting projects, we’d love to have you in the community! 🤝

Check out some of our other work:

  1. 🐫 Creating Your First CAMEL Agent free Colab

  2. Graph RAG Cookbook free Colab

  3. 🧑‍⚖️ Create A Hackathon Judge Committee with Workforce free Colab

  4. 🔥 3 ways to ingest data from websites with Firecrawl & CAMEL free Colab

  5. 🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth free Colab

Thanks from everyone at 🐫 CAMEL-AI

⭐ Star us on GitHub, join our Discord, or follow us on X