Message Cookbook

You can also check this cookbook in colab here

Overview

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

Introduction

The BaseMessage class is 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.

Installation

Ensure you have CAMEL AI installed in your Python environment:

!pip install "camel-ai==0.2.9"

Setting Up API Keys

You’ll need to set up your API keys for OpenAI.

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

Enter your 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)

Hello, CAMEL AI! It's great to connect with a community dedicated to the study of autonomous and communicative agents. Your work in this fascinating field is important for advancing our understanding of AI and its capabilities. If you have any questions or topics you'd like to discuss, feel free to share!

Give message to ChatAgent via BaseMessage

For more complex message usage like mulit-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",
)

2. 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))

True

2. 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"})

True

3. Converting to an OpenAISystemMessage object:

openai_system_message = message.to_openai_system_message()
print(openai_system_message == {"role": "system", "content": "test content"})

True

4. Converting to an OpenAIUserMessage object:

openai_user_message = message.to_openai_user_message()
print(openai_user_message == {"role": "user", "content": "test content"})

True

5. Converting to an OpenAIAssistantMessage object:

openai_assistant_message = message.to_openai_assistant_message()
print(openai_assistant_message == {"role": "assistant", "content": "test content"})

True

6. Converting to a dictionary:

message_dict = message.to_dict()
print(message_dict == {
    "role_name": "test_user",
    "role_type": "USER",
    "content": "test content"
})

True

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)

The image features a logo for "CAMEL-AI." It includes a stylized purple camel graphic alongside the text "CAMEL-AI," which is also in purple. The design appears modern and is likely related to artificial intelligence.

In this session, we introduced the BaseMessage class and its conversion 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.