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

Install the package using:
```
uv pip install .
```
Launch the App: Start the application using Streamlit:
```
medrag run
```
Configure Settings: Adjust configuration settings in the sidebar to suit your needs.
Ask a Question: Enter your medical question in the chat input field.
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: