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Agent As A Judge
Overview :
Agent-as-a-Judge is a new type of automated evaluation system designed to improve work efficiency and quality through mutual evaluations by proxy systems. This product significantly reduces evaluation time and cost while providing continuous feedback signals to promote self-improvement of the proxy systems. It is widely used in AI development tasks, especially in the field of code generation. The system has open-source characteristics, making it easy for developers to carry out secondary development and customization.
Target Users :
Suitable for AI developers, researchers, and enterprise teams, especially users who need to quickly and efficiently conduct project evaluations and feedback. This product can help them save time and reduce costs in complex development environments while improving code quality and project success rates.
Use Cases
Use Agent-as-a-Judge for code generation task evaluation to improve development efficiency.
Utilize this tool to automatically evaluate student projects in AI teaching and provide instant feedback.
Integrate Agent-as-a-Judge into internal development processes within enterprises to achieve efficient code quality assessment.
Features
Automatic evaluation: significantly save evaluation time and cost.
Reward signal provision: continuous feedback promotes self-improvement.
Supports calling multiple large language models (LLMs).
User-friendly command-line interface, convenient for quick start.
Strong scalability, suitable for different development needs.
Open source code, supports community contributions and improvements.
Integrates multiple evaluation standards to enhance evaluation accuracy.
Supports compatibility with multiple development platforms.
How to Use
Clone the code repository: git clone https://github.com/metauto-ai/agent-as-a-judge.git
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Create a virtual environment and activate it: conda create -n aaaj python=3.11 && conda activate aaaj
Install dependencies: pip install poetry && poetry install
Set environment variables: Rename .env.sample to .env and fill in the required APIs.
Run example scripts to test functionality: PYTHONPATH=. python scripts/run_ask.py --workspace YOUR_WORKSPACE --question 'YOUR_QUESTION'
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