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Repository for: Autonomous AI Agents for Clinical Decision Making in Oncology ⚠️ This repository is currently under construction. Usage might change in the future. ⚠️ The current agent implementation uses test functions for the image segmentation and genetic modeling tasks as the original implementation requires external repositories that are challenging to setup. We are working on a solution to simplify their setup in the very near future. The provided test functions () are implemented as agent tools without any changes to their original implementation () and have therefore no influence on the LLM-Agents behaviour. Software Requirements All experiments were run on an Apple MacBook Pro M2 Max 96GB 2023. No special hardware is required, if you wish to run certain models with hardware acceleration, it is recommended to have a CUDA-compatible GPU to speed up the process. General Setup Instructions Please follow the steps below: Python Installation: Install Python from source. We used Python 3.11.6 throughout this project. Dependency Installation: Clone this repository: This process might take around 1 minute. Set up a clean python3 virtual environment, i.e.
| Stars | 63 |
| Forks | 9 |
| Language | Python |
| Category | AI Tool |
| License | MIT |
| Quality Score | 55.2795245262964/100 |
| Open Issues | 2 |
| Last Updated | 2024-07-18 |
| Created | 2024-04-24 |
| Platforms | python |
| Est. Tokens | ~41k |
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LLM_RAG_Agent is an open-source ai tool by Dyke-F with 63 GitHub stars.
LLM_RAG_Agent is primarily written in Python.
You can find installation instructions and usage details in the LLM_RAG_Agent GitHub repository at github.com/Dyke-F/LLM_RAG_Agent. The project has 63 stars and 9 forks, indicating an active community.
LLM_RAG_Agent is released under the MIT license, making it free to use and modify according to the license terms.
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