Project Introduction to EvalGPT
What is EvalGPT?
EvalGPT is an innovative project designed to work as a code interpreter framework, which harnesses the capabilities of large language models like GPT-4, CodeLlama, and Claude 2. As the project is still in its early stages of development, contributions and suggestions from users are welcomed. EvalGPT helps users by taking their tasks, assisting in writing the code needed, executing it, and ultimately delivering the desired results.
How Does EvalGPT Work?
The architecture of EvalGPT is borrowed from Google's Borg system and is structured around a central control unit known as the EvalGPT master node. This node is composed of three important elements: planning, scheduler, and memory.
When a user sends a request, EvalGPT's planning component immediately kicks into action, using its language models to take apart large tasks into smaller, more manageable chunks. Each of these sub-tasks is then assigned to a node called an EvalAgent.
These EvalAgent nodes are responsible for generating code to carry out their particular tasks. Once the code is generated, it is executed, even using necessary external tools when required, and the results are collected. They communicate with the EvalGPT master node for memory access, allowing for smooth data exchange and interaction. If any errors occur, they report them to the master, allowing for the task to be replanned.
Benefits of Using EvalGPT
- Automated Code Writing: It alleviates the manual coding process by using advanced language models, thus increasing productivity.
- Efficient Task Execution: Tasks are broken into sub-tasks for parallel execution, speeding up the process.
- Robust Error Handling: With task replanning, EvalGPT ensures reliable operation and accuracy.
- Scalability: It is designed to handle varying task complexities, making it highly scalable.
- Resource Optimization: Inspired by Google's Borg, it optimizes computational resource use.
- Extensibility: It can incorporate external tools, making it highly adaptable for diverse tasks.
Getting Started
To get started with EvalGPT, it can be installed using the Go programming language. Users need to set up configurations including the OpenAI API Key and the Python interpreter for its execution.
Usage Examples
EvalGPT can be used for a variety of queries, tasks, and operations such as retrieving public IP addresses, calculating hashes, retrieving website titles, converting file types, and processing data from CSV files.
Architecture Details
- EvalGPT Master Node: Acts as the framework's control center, involving task planning, scheduling, and sharing memory across nodes.
- EvalAgent Node: Work units that generate code, execute it, and return results, ensuring efficient handling of tasks.
- Runtime: Managed by EvalAgents to ensure adaptable and error-free execution environments.
The EvalGPT framework is thus a comprehensive, powerful tool aimed at automating and optimizing coding tasks through its advanced architecture and language model capabilities.