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| # MedQA Assistant App | |
| The MedQA Assistant App is a Streamlit-based application designed to provide a chat interface for medical question answering. It leverages advanced language models (LLMs) and retrieval augmented generation (RAG) techniques to deliver accurate and informative responses to medical queries. | |
| ## Features | |
| - **Interactive Chat Interface**: Engage with the app through a user-friendly chat interface. | |
| - **Configurable Settings**: Customize model selection and data sources via the sidebar. | |
| - **Retrieval-Augmented Generation**: Ensures precise and contextually relevant responses. | |
| - **Figure Annotation Capabilities**: Extracts and annotates figures from medical texts. | |
| ## Usage | |
| 1. Install the package using: | |
| ```bash | |
| uv pip install . | |
| ``` | |
| 1. **Launch the App**: Start the application using Streamlit: | |
| ```bash | |
| medrag run | |
| ``` | |
| 2. **Configure Settings**: Adjust configuration settings in the sidebar to suit your needs. | |
| 3. **Ask a Question**: Enter your medical question in the chat input field. | |
| 4. **Receive a Response**: Get a detailed answer from the MedQA Assistant. | |
| ## Configuration | |
| The app allows users to customize various settings through the sidebar: | |
| - **Project Name**: Specify the WandB project name. | |
| - **Text Chunk WandB Dataset Name**: Define the dataset containing text chunks. | |
| - **WandB Index Artifact Address**: Provide the address of the index artifact. | |
| - **WandB Image Artifact Address**: Provide the address of the image artifact. | |
| - **LLM Client Model Name**: Choose a language model for generating responses. | |
| - **Figure Extraction Model Name**: Select a model for extracting figures from images. | |
| - **Structured Output Model Name**: Choose a model for generating structured outputs. | |
| ## Technical Details | |
| The app is built using the following components: | |
| - **Streamlit**: For the user interface. | |
| - **Weave**: For project initialization and artifact management. | |
| - **MedQAAssistant**: For processing queries and generating responses. | |
| - **LLMClient**: For interacting with language models. | |
| - **MedCPTRetriever**: For retrieving relevant text chunks. | |
| - **FigureAnnotatorFromPageImage**: For annotating figures in medical texts. | |
| ## Development and Deployment | |
| - **Environment Setup**: Ensure all dependencies are installed as per the `pyproject.toml`. | |
| - **Running the App**: Use Streamlit to run the app locally. | |
| - **Deployment**: coming soon... | |
| ## Additional Resources | |
| For more detailed information on the components and their usage, refer to the following documentation sections: | |
| - [MedQA Assistant](/assistant/medqa_assistant) | |
| - [LLM Client](/assistant/llm_client) | |
| - [Figure Annotation](/assistant/figure_annotation) | |