added read me to app

app/README.md

@@ -0,0 +1,74 @@
+# MkDocs Chatbot App
+
+A Streamlit-based chatbot application that provides an interactive AI-powered interface to chat with the mkdocs-chatbot plugin documentation.
+
+## Overview
+
+This application uses LlamaIndex and Google GenAI to create a conversational interface that allows users to ask questions about the mkdocs-chatbot plugin and receive intelligent, context-aware answers based on the documentation.
+
+## Features
+
+- 🤖 AI-powered Q&A using Google GenAI
+- 📚 Vector-based document retrieval from markdown files
+- 💬 Interactive chat interface built with Streamlit
+- 🔍 Context-aware responses based on plugin documentation
+
+## Requirements
+
+- Python >=3.12, <4.0
+- Google GenAI API key
+- Streamlit account (for deployment) or local Streamlit installation
+
+## Installation
+
+1. Install dependencies:
+```bash
+uv sync
+```
+
+Or with pip:
+```bash
+pip install -e .
+```
+
+## Configuration
+
+The app requires the following secrets to be configured in Streamlit:
+
+- `GOOGLE_API_KEY`: Your Google GenAI API key
+- `GOOGLE_LLM_MODEL`: The Google GenAI LLM model to use (e.g., "gemini-pro")
+- `GOOGLE_EMB_MODEL`: The Google GenAI embedding model to use (e.g., "models/embedding-001")
+
+### Setting up Streamlit Secrets
+
+For local development, create a `.streamlit/secrets.toml` file in your project root:
+
+```toml
+GOOGLE_API_KEY = "your-api-key-here"
+GOOGLE_LLM_MODEL = "gemini-pro"
+GOOGLE_EMB_MODEL = "models/embedding-001"
+```
+
+For Streamlit Cloud deployment, configure these secrets in your app settings.
+
+## Running the App
+
+### Local Development
+
+```bash
+streamlit run main.py
+```
+
+The app will be available at `http://localhost:8501`
+
+### Streamlit Cloud
+
+Deploy the app to Streamlit Cloud by connecting your GitHub repository and configuring the secrets in the app settings.
+
+## How It Works
+
+1. **Document Loading**: The app loads all markdown files from the `docs/` directory
+2. **Indexing**: Documents are processed and indexed into a vector store using Google GenAI embeddings
+3. **Chat Interface**: Users can ask questions through the Streamlit chat interface
+4. **Query Processing**: Questions are processed using the vector index to find relevant documentation
+5. **Response Generation**: The LLM generates context-aware answers based on the retrieved documentation