PR- 2407

RAGChatbot/.gitignore

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+**/.env
+**/test.txt

RAGChatbot/README.md

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+## RAG Chatbot
+
+A full-stack Retrieval-Augmented Generation (RAG) application that enables intelligent, document-based question answering.
+The system integrates a FastAPI backend powered by LangChain, FAISS, and AI models, alongside a modern React + Vite + Tailwind CSS frontend for an intuitive chat experience.
+
+## Table of Contents
+
+- [Project Overview](#project-overview)
+- [Features](#features)
+- [Architecture](#architecture)
+- [Prerequisites](#prerequisites)
+- [Quick Start Deployment](#quick-start-deployment)
+- [User Interface](#user-interface)
+- [Troubleshooting](#troubleshooting)
+
+---
+
+## Project Overview
+
+The **RAG Chatbot** demonstrates how retrieval-augmented generation can be used to build intelligent, document-grounded conversational systems. It retrieves relevant information from a knowledge base, passes it to a large language model, and generates a concise and reliable answer to the user’s query. This project integrates seamlessly with cloud-hosted APIs or local model endpoints, offering flexibility for research, enterprise, or educational use.
+
+---
+
+## Features
+
+**Backend**
+
+- Clean PDF upload with validation
+- LangChain-powered document processing
+- FAISS-CPU vector store for efficient similarity search
+- Enterprise inference endpoints for embeddings and LLM
+- Keycloak authentication for secure API access
+- Comprehensive error handling and logging
+- File validation and size limits
+- CORS enabled for web integration
+- Health check endpoints
+- Modular architecture (routes + services)
+
+**Frontend**
+
+- PDF file upload with drag-and-drop support
+- Real-time chat interface
+- Modern, responsive design with Tailwind CSS
+- Built with Vite for fast development
+- Live status updates
+- Mobile-friendly
+
+---
+
+## Architecture
+
+Below is the architecture as it consists of a server that waits for documents to embed and index into a vector database. Once documents have been uploaded, the server will wait for user queries which initiates a similarity search in the vector database before calling the LLM service to summarize the findings.
+
+![Architecture Diagram](./images/RAG%20Model%20System%20Design.png)
+
+**Service Components:**
+
+1. **React Web UI (Port 3000)** - Provides intuitive chat interface with drag-and-drop PDF upload, real-time messaging, and document-grounded Q&A interaction
+
+2. **FastAPI Backend (Port 5001)** - Handles document processing, FAISS vector storage, LangChain integration, and orchestrates retrieval-augmented generation for accurate responses
+
+**Typical Flow:**
+
+1. User uploads a document through the web UI.
+2. The backend processes the document by splitting it and transforming it into embeddings before storing it in the vector database.
+3. User sends a question through the web UI.
+4. The backend retrieves relevant content from stored documents.
+5. The model generates a response based on retrieved context.
+6. The answer is displayed to the user via the UI.
+
+---
+
+## Prerequisites
+
+### System Requirements
+
+Before you begin, ensure you have the following installed:
+
+- **Docker and Docker Compose**
+- **Enterprise inference endpoint access** (Keycloak authentication)
+
+### Verify Docker Installation
+
+```bash
+# Check Docker version
+docker --version
+
+# Check Docker Compose version
+docker compose version
+
+# Verify Docker is running
+docker ps
+```
+
+## Quick Start Deployment
+
+### Clone the Repository
+
+```bash
+git clone https://github.com/opea-project/GenAIExamples.git
+cd GenAIExamples/RAGChatbot
+```
+
+### Set up the Environment
+
+This application requires an `.env` file in the `api` directory for proper configuration. Create it with the commands below:
+
+```bash
+# Create the .env file in the api directory
+mkdir -p api
+cat > api/.env << EOF
+# Backend API URL (accessible from frontend)
+VITE_API_URL=https://backend:5000
+
+# Required - Enterprise/Keycloak Configuration
+BASE_URL=https://api.example.com
+KEYCLOAK_REALM=master
+KEYCLOAK_CLIENT_ID=api
+KEYCLOAK_CLIENT_SECRET=your_client_secret
+
+# Required - Model Configuration
+EMBEDDING_MODEL_ENDPOINT=bge-base-en-v1.5
+INFERENCE_MODEL_ENDPOINT=Llama-3.1-8B-Instruct
+EMBEDDING_MODEL_NAME=bge-base-en-v1.5
+INFERENCE_MODEL_NAME=meta-llama/Llama-3.1-8B-Instruct
+EOF
+```
+
+Or manually create `api/.env` with:
+
+```bash
+# Backend API URL (accessible from frontend)
+VITE_API_URL=https://backend:5000
+
+# Required - Enterprise/Keycloak Configuration
+BASE_URL=https://api.example.com
+KEYCLOAK_REALM=master
+KEYCLOAK_CLIENT_ID=api
+KEYCLOAK_CLIENT_SECRET=your_client_secret
+
+# Required - Model Configuration
+EMBEDDING_MODEL_ENDPOINT=bge-base-en-v1.5
+INFERENCE_MODEL_ENDPOINT=Llama-3.1-8B-Instruct
+EMBEDDING_MODEL_NAME=bge-base-en-v1.5
+INFERENCE_MODEL_NAME=meta-llama/Llama-3.1-8B-Instruct
+```
+
+**Note**: The docker-compose.yml file automatically loads environment variables from `./api/.env` for the backend service.
+
+### Running the Application
+
+Start both API and UI services together with Docker Compose:
+
+```bash
+# From the rag-chatbot directory
+docker compose up --build
+
+# Or run in detached mode (background)
+docker compose up -d --build
+```
+
+The API will be available at: `http://localhost:5001`  
+The UI will be available at: `http://localhost:3000`
+
+**View logs**:
+
+```bash
+# All services
+docker compose logs -f
+
+# Backend only
+docker compose logs -f backend
+
+# Frontend only
+docker compose logs -f frontend
+```
+
+**Verify the services are running**:
+
+```bash
+# Check API health
+curl http://localhost:5001/health
+
+# Check if containers are running
+docker compose ps
+```
+
+## User Interface
+
+**Using the Application**
+
+Make sure you are at the localhost:3000 url
+
+You will be directed to the main page which has each feature
+
+![User Interface](images/ui.png)
+
+Upload a PDF:
+
+- Drag and drop a PDF file, or
+- Click "Browse Files" to select a file
+- Wait for processing to complete
+
+Start chatting:
+
+- Type your question in the input field
+- Press Enter or click Send
+- Get AI-powered answers based on your document
+
+**UI Configuration**
+
+When running with Docker Compose, the UI automatically connects to the backend API. The frontend is available at `http://localhost:3000` and the API at `http://localhost:5001`.
+
+For production deployments, you may want to configure a reverse proxy or update the API URL in the frontend configuration.
+
+### Stopping the Application
+
+
+```bash
+docker compose down
+```
+
+## Troubleshooting
+
+For comprehensive troubleshooting guidance, common issues, and solutions, refer to:
+
+[Troubleshooting Guide - TROUBLESHOOTING.md](./TROUBLESHOOTING.md)

RAGChatbot/TROUBLESHOOTING.md

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+# Troubleshooting Guide
+
+This document contains all common issues encountered during development and their solutions.
+
+## Table of Contents
+
+- [API Common Issues](#api-common-issues)
+- [UI Common Issues](#ui-common-issues)
+
+### API Common Issues
+
+#### "OPENAI_API_KEY not found in environment variables"
+
+**Solution**:
+
+1. Create a `.env` file in the `api` directory
+2. Add your OpenAI API key: `OPENAI_API_KEY=your_key_here`
+3. Restart the server
+
+#### "No documents uploaded"
+
+**Solution**:
+
+- Upload a PDF first using the `/upload-pdf` endpoint
+- Check server logs for any upload errors
+- Verify the PDF is not corrupted or empty
+
+#### "Could not load vector store"
+
+**Solution**:
+
+- The vector store is created when you upload your first PDF
+- Check that the application has write permissions in the directory
+- Verify `dmv_index/` directory exists and is accessible
+
+#### Import errors
+
+**Solution**:
+
+1. Ensure all dependencies are installed: `pip install -r requirements.txt`
+2. Verify you're using Python 3.10 or higher: `python --version`
+3. Activate your virtual environment if using one
+
+#### Server won't start
+
+**Solution**:
+
+1. Check if port 5000 is already in use: `lsof -i :5000` (Unix) or `netstat -ano | findstr :5000` (Windows)
+2. Use a different port: `uvicorn server:app --port 5001`
+3. Check the logs for specific error messages
+
+#### PDF upload fails
+
+**Solution**:
+
+1. Verify the file is a valid PDF
+2. Check file size (must be under 50MB by default)
+3. Ensure the PDF contains extractable text (not just images)
+4. Check server logs for detailed error messages
+
+#### Query returns no answer
+
+**Solution**:
+
+1. Verify a document has been uploaded successfully
+2. Try rephrasing your question
+3. Check if the document contains relevant information
+4. Increase `TOP_K_DOCUMENTS` in `config.py` for broader search
+
+## UI Common Issues
+
+### API Connection Issues
+
+**Problem**: "Failed to upload PDF" or "Failed to get response"
+
+**Solution**:
+
+1. Ensure the API server is running on `http://localhost:5000`
+2. Check browser console for detailed errors
+3. Verify CORS is enabled in the API
+
+### Build Issues
+
+**Problem**: Build fails with dependency errors
+
+**Solution**:
+
+```bash
+# Clear node_modules and reinstall
+rm -rf node_modules package-lock.json
+npm install
+```
+
+### Styling Issues
+
+**Problem**: Styles not applying
+
+**Solution**:
+
+```bash
+# Rebuild Tailwind CSS
+npm run dev
+```

RAGChatbot/api/Dockerfile

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+FROM python:3.9-slim
+
+# Set the working directory in the container
+WORKDIR /app
+
+COPY requirements.txt .
+
+
+# Install Python dependencies
+RUN pip install --no-cache-dir -r requirements.txt
+
+# Copy the rest of the application files into the container
+COPY server.py .
+
+# Expose the port the service runs on
+EXPOSE 5001
+
+# Command to run the application
+CMD ["uvicorn", "server:app", "--host", "0.0.0.0", "--port", "5001", "--reload"]