> ## Documentation Index
> Fetch the complete documentation index at: https://docs.camel-ai.org/llms.txt
> Use this file to discover all available pages before exploring further.
# Message Cookbook
You can also check this cookbook in colab [here](https://colab.research.google.com/drive/1qyi4bnAbnYink-FKaAlJG9OipyEWXEsT?usp=sharing).
⭐ *Star us on [GitHub](https://github.com/camel-ai/camel), join our [Discord](https://discord.camel-ai.org), or follow us on [X](https://x.com/camelaiorg)*
***
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:
```python theme={"system"}
!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.
```python theme={"system"}
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](https://app.agentops.ai/signin) to get **free** API Key from AgentOps
```python theme={"system"}
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](https://console.mistral.ai/api-keys/) to get API Key from Mistral AI with **free** credits.
```python theme={"system"}
# 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.
```python theme={"system"}
# 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`
```python theme={"system"}
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:
```python theme={"system"}
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:
```python theme={"system"}
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:
```python theme={"system"}
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:
```python theme={"system"}
new_message = message.create_new_instance("new test content")
print(isinstance(new_message, BaseMessage))
```
2. Converting to an `OpenAIMessage` object:
```python theme={"system"}
from camel.types import OpenAIBackendRole
openai_message = message.to_openai_message(role_at_backend=OpenAIBackendRole.USER)
print(openai_message == {"role": "user", "content": "test content"})
```
3. Converting to an `OpenAISystemMessage` object:
```python theme={"system"}
openai_system_message = message.to_openai_system_message()
print(openai_system_message == {"role": "system", "content": "test content"})
```
4. Converting to an `OpenAIUserMessage` object:
```python theme={"system"}
openai_user_message = message.to_openai_user_message()
print(openai_user_message == {"role": "user", "content": "test content"})
```
5. Converting to an `OpenAIAssistantMessage` object:
```python theme={"system"}
openai_assistant_message = message.to_openai_assistant_message()
print(openai_assistant_message == {"role": "assistant", "content": "test content"})
```
6. Converting to a dictionary:
```python theme={"system"}
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`
```python theme={"system"}
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](https://discord.camel-ai.org)! 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](https://docs.camel-ai.org/cookbooks/create_your_first_agent.html)
2. Graph RAG Cookbook [free Colab](https://colab.research.google.com/drive/1uZKQSuu0qW6ukkuSv9TukLB9bVaS1H0U?usp=sharing)
3. 🧑⚖️ Create A Hackathon Judge Committee with Workforce [free Colab](https://colab.research.google.com/drive/18ajYUMfwDx3WyrjHow3EvUMpKQDcrLtr?usp=sharing)
4. 🔥 3 ways to ingest data from websites with Firecrawl & CAMEL [free Colab](https://colab.research.google.com/drive/1lOmM3VmgR1hLwDKdeLGFve_75RFW0R9I?usp=sharing)
5. 🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth [free Colab](https://colab.research.google.com/drive/1lYgArBw7ARVPSpdwgKLYnp_NEXiNDOd-?usp=sharingg)
Thanks from everyone at 🐫 CAMEL-AI
⭐ *Star us on [GitHub](https://github.com/camel-ai/camel), join our [Discord](https://discord.camel-ai.org), or follow us on [X](https://x.com/camelaiorg)*
***
