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 Prompt module.

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.

  • Prompt: Interface to communicate with models with various templates, create custom prompts, and leverage different prompt dictionaries for tasks ranging from role-playing to code generation, evaluation, and more. By mastering the Prompt module, you can significantly enhance your AI agents’ capabilities and tailor them to specific tasks.

​
📦 Installation

Ensure you have CAMEL AI installed in your Python environment:

!pip install "camel-ai==0.2.16"

​
🔑 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

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")

​
Getting Started with Prompt Templates

CAMEL offers a wide range of pre-defined prompt templates that you can use to quickly create specialized AI agents. Let’s start with a basic example using the TaskSpecifyAgent with the AI_SOCIETY task type.

from camel.agents import TaskSpecifyAgent
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory
from camel.types import ModelPlatformType, ModelType, TaskType

# Set up the model
model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)

# Create a task specify agent
task_specify_agent = TaskSpecifyAgent(
    model=model, task_type=TaskType.AI_SOCIETY
)

# Run the agent with a task prompt
specified_task_prompt = task_specify_agent.run(
    task_prompt="Improving stage presence and performance skills",
    meta_dict=dict(
        assistant_role="Musician", user_role="Student", word_limit=100
    ),
)

print(f"Specified task prompt:\n{specified_task_prompt}\n")

​
Creating Custom Prompts

CAMEL also allows you to create your own custom prompts. Here’s an example of how to create and use a custom prompt template:

from camel.agents import TaskSpecifyAgent
from camel.configs import ChatGPTConfig
from camel.models import ModelFactory
from camel.prompts import TextPrompt
from camel.types import ModelPlatformType, ModelType

# Set up the model
model = ModelFactory.create(
    model_platform=ModelPlatformType.OPENAI,
    model_type=ModelType.GPT_4O_MINI,
)

# Create a custom prompt template
my_prompt_template = TextPrompt(
    'Here is a task: I\'m a {occupation} and I want to {task}. Help me to make this task more specific.'
)

# Create a task specify agent with the custom prompt
task_specify_agent = TaskSpecifyAgent(
    model=model, task_specify_prompt=my_prompt_template
)

# Run the agent with a task prompt
response = task_specify_agent.run(
    task_prompt="get promotion",
    meta_dict=dict(occupation="Software Engineer"),
)

print(response)

​
Advanced Prompt Usage

CAMEL provides various prompt dictionaries for different purposes. Let’s explore some advanced uses of these prompt templates:

​
1. Code Generation with CodePromptTemplateDict

from camel.prompts import CodePromptTemplateDict

# Generate programming languages
languages_prompt = CodePromptTemplateDict.GENERATE_LANGUAGES.format(num_languages=5)
print(f"Languages prompt:\n{languages_prompt}\n")

# Generate coding tasks
tasks_prompt = CodePromptTemplateDict.GENERATE_TASKS.format(num_tasks=3)
print(f"Tasks prompt:\n{tasks_prompt}\n")

# Create an AI coding assistant prompt
assistant_prompt = CodePromptTemplateDict.ASSISTANT_PROMPT.format(
    assistant_role="Python Expert",
    task_description="Implement a binary search algorithm",
)
print(f"Assistant prompt:\n{assistant_prompt}\n")

​
2. Evaluation with EvaluationPromptTemplateDict

from camel.prompts import EvaluationPromptTemplateDict

# Generate evaluation questions
questions_prompt = EvaluationPromptTemplateDict.GENERATE_QUESTIONS.format(
    num_questions=5,
    field="Machine Learning",
    examples="1. What is the difference between supervised and unsupervised learning?\n2. Explain the concept of overfitting.",
)
print(f"Evaluation questions prompt:\n{questions_prompt}\n")

​
3. Object Recognition with ObjectRecognitionPromptTemplateDict

from camel.prompts import ObjectRecognitionPromptTemplateDict

# Create an object recognition assistant prompt
recognition_prompt = ObjectRecognitionPromptTemplateDict.ASSISTANT_PROMPT
print(f"Object recognition prompt:\n{recognition_prompt}\n")

​
4. Translation with TranslationPromptTemplateDict

from camel.prompts import TranslationPromptTemplateDict

# Create a translation assistant prompt
translation_prompt = TranslationPromptTemplateDict.ASSISTANT_PROMPT.format(target_language="Spanish")
print(f"Translation prompt:\n{translation_prompt}\n")

​
🌟 Highlights

This notebook has guided you through setting up and use Prompt module. The CAMEL Prompt module provides a powerful and flexible way to guide AI models in producing desired outputs. By using pre-defined prompt templates, creating custom prompts, and leveraging different prompt dictionaries, you can create highly specialized AI agents tailored to your specific needs.

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.
  • Prompt: Interface to communicate with models with various templates, create custom prompts, and leverage different prompt dictionaries for tasks ranging from role-playing to code generation, evaluation, and more. By mastering the Prompt module, you can significantly enhance your AI agents’ capabilities and tailor them to specific tasks.

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

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