add: documentations for MedQA Assistant App

docs/app.md

@@ -0,0 +1,57 @@
+# 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. **Launch the App**: Start the application using Streamlit:
+    ```bash
+    streamlit run app.py
+    ```
+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)

mkdocs.yml

@@ -62,6 +62,8 @@ nav:
   - Setup:
     - Installation: 'installation/install.md'
     - Development: 'installation/development.md'
+  - App:
+    - MedQA Assistant: 'app.md'
   - Document Loader:
     - Text Loader:
       - Base: 'document_loader/text_loader/base_text_loader.md'