Camel-Coder

Camel-Coder is an advanced Python script powered by OpenAI's GPT-3.5-turbo model, designed to enable robust task-oriented chatbot conversations. It leverages the role-playing conversation capabilities of the GPT model to assign specific roles to different chat agents, guiding them towards achieving a specific task collaboratively.

Features

• Multiple Agents: Camel-Coder provides support for multi-agent interactions. Each agent is assigned a unique role with specific responsibilities, adding depth and versatility to the chatbot capabilities.

• Task-Oriented Conversation: The chatbot is designed to steer the conversation towards the completion of a specific task, facilitating goal-oriented dialogue.

• Role-Based Prompts: Camel-Coder introduces role-specific prompt templates for each agent, offering instructions and guidelines that are tailored to each agent's function in the task completion process.

• Intervention Mechanism: The script incorporates an innovative intervention mechanism. A special monitor agent can intervene in the conversation to ensure the conversation remains within the bounds of the set objective.

• Thoughtful Agent: The script also features a thoughtful agent that proactively provides suggestions and guidance to drive the conversation in the right direction.

• Coding Agent: This agent generates functional prototypes based on the discussion in the conversation, providing a practical outcome from the task-oriented dialogue.

• Conversation Saving: It offers an automatic saving of the complete conversation to a text file, making it easy to review, analyze, or audit the conversation at any later stage.

• Token Counting and Cost Estimation: To ensure transparency and cost-effectiveness, the script counts the tokens used in a conversation and provides a corresponding cost estimate.

Setup

1. Dependencies: Install the required dependencies using pip:

pip install openai langchain

markdown

2. Environment Variables: Set up the required environment variables. This primarily includes the OpenAI API key.

3. Role-Specific Prompts: Define role-specific prompts for each agent by tweaking the assistant_role_name, user_role_name, and task variables in the CamelCoder.py script.

4. Execution: Run the CamelCoder.py script to start the chatbot.

File Structure and Workspace

Camel-Coder establishes a directory structure within the workspace to categorize the generated code and related files.

Conversation Saving

To configure the automatic conversation-saving feature, follow the below steps:

1. Open the CamelCoder.py script.
2. Find the conversation_directory variable.
3. Replace the current path with the desired directory where you wish to save the conversation file:

conversation_directory = "/your/desired/directory"

bash

Replace /your/desired/directory with the actual path where you want the conversation file to be saved.

Token Counting and Cost Estimation

After executing the Camel-Coder script, the console output will provide information about the token count and cost estimation.

License

This project is licensed under the terms of a Custom GNU License.