SharePoint AI Assistant

An intelligent assistant that transforms how you interact with SharePoint content through natural language conversations. Simply ask questions about your documents, and get instant, contextual answers powered by advanced AI.

✨ What It Does

Transform your SharePoint experience with conversational AI that understands your content:

• Natural Conversations: Ask questions about your SharePoint documents in plain English
• Smart Document Discovery: Find relevant files across your SharePoint sites effortlessly
• Instant Previews: View and analyze PDF, Word, and Excel files without leaving the chat
• Contextual Answers: Get precise responses based on your actual document content

Built for reliability with enterprise-grade security, comprehensive error handling, and production-ready architecture that scales with your organization's needs.

📁 Project Structure

sharepoint-ai-assistant/
├── src/                          # Main application source code
│   ├── core/                     # Core functionality and configuration
│   │   ├── __init__.py
│   │   ├── config.py            # Configuration management
│   │   ├── constants.py         # Application constants
│   │   ├── exceptions.py        # Custom exception classes
│   │   └── logging_config.py    # Logging configuration
│   ├── clients/                  # External service clients
│   │   ├── __init__.py
│   │   └── sharepoint_client.py # Enhanced SharePoint client
│   ├── services/                 # Business logic services
│   │   ├── __init__.py
│   │   └── llm_service.py       # LLM service with error handling
│   ├── utils/                    # Utility functions
│   │   ├── __init__.py
│   │   ├── validation.py        # Input validation and security
│   │   └── file_utils.py        # File processing utilities
│   └── ui/                       # User interface
│       ├── __init__.py
│       └── main.py              # Enhanced Streamlit UI
├── tests/                        # Test suite
│   ├── unit/                     # Unit tests
│   │   └── test_validation.py   # Validation tests
│   └── integration/              # Integration tests
├── requirements/                 # Dependency management
│   ├── base.txt                 # Base requirements
│   └── dev.txt                  # Development requirements
├── logs/                         # Application logs
├── data/                         # Data directory
├── main.py                       # Application entry point
├── Dockerfile                    # Enhanced Docker configuration
├── docker-compose.yml           # Docker Compose setup
└── README.md                     # This file

🛠️ Quick Start - Python Environment Setup

Prerequisites

• Python 3.11 (required)
• Git
• Ollama (for local LLM support)
• Docker (optional, for containerized deployment)

⚡ Simple Manual Setup (5 Steps)

# 1. Clone and navigate to the project
git clone https://github.com/urdiales/sharepoint-ai-assistant.git
cd sharepoint-ai-assistant

# 2. Create Python 3.11 virtual environment
python3.11 -m venv venv

# 3. Activate environment and install dependencies
# Windows:
venv\Scripts\activate && pip install -r requirements/dev.txt
# macOS/Linux:
source venv/bin/activate && pip install -r requirements/dev.txt

# 4. Set up configuration
cp .env.example .env
# Edit .env with your SharePoint credentials

# 5. Run the application
streamlit run main.py

🚀 Super Quick Setup (Automated)

For the easiest setup experience, use our interactive setup script:

# Clone the repository
git clone https://github.com/urdiales/sharepoint-ai-assistant.git
cd sharepoint-ai-assistant

# Run the setup script
python setup.py

The script will:

• ✅ Check your Python version
• 🔍 Detect available environment managers (Conda, UV, venv)
• 📋 Guide you through setup options
• ⚙️ Create your environment automatically
• 📄 Copy configuration files

🛠️ Manual Setup Options

🎯 Option A: Anaconda/Miniconda (Recommended for Beginners)

Step 1: Install Anaconda/Miniconda

• Download from anaconda.com or miniconda

Step 2: Create and activate environment

# Clone the repository
git clone https://github.com/urdiales/sharepoint-ai-assistant.git
cd sharepoint-ai-assistant

# Create environment with Python 3.11
conda create -n sharepoint-ai python=3.11 -y
conda activate sharepoint-ai

# Install dependencies
pip install -r requirements/dev.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the application
streamlit run main.py

⚡ Option B: UV (Fast & Modern)

Step 1: Install UV

# On Windows (PowerShell)
powershell -c "irm https://astral.sh/uv/install.ps1 | iex"

# On macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh

Step 2: Set up project

# Clone the repository
git clone https://github.com/urdiales/sharepoint-ai-assistant.git
cd sharepoint-ai-assistant

# Create virtual environment with Python 3.11
uv venv --python 3.11

# Activate environment
# On Windows:
.venv\Scripts\activate
# On macOS/Linux:
source .venv/bin/activate

# Install dependencies (lightning fast!)
uv pip install -r requirements/dev.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the application
streamlit run main.py

🐍 Option C: Traditional Python venv

Step 1: Verify Python 3.11

python3.11 --version  # Should show Python 3.11.x
# If not available, install Python 3.11 from python.org

Step 2: Set up project

# Clone the repository
git clone https://github.com/urdiales/sharepoint-ai-assistant.git
cd sharepoint-ai-assistant

# Create virtual environment
python3.11 -m venv venv

# Activate environment
# On Windows:
venv\Scripts\activate
# On macOS/Linux:
source venv/bin/activate

# Upgrade pip and install dependencies
pip install --upgrade pip
pip install -r requirements/dev.txt

# Set up environment variables
cp .env.example .env
# Edit .env with your configuration

# Run the application
streamlit run main.py

🔍 Quick Verification

After setup, verify everything works:

# Check Python version
python --version  # Should show Python 3.11.x

# Test import of key dependencies
python -c "import streamlit, pandas, langchain; print('✅ All dependencies installed successfully!')"

# Check if Streamlit runs
streamlit hello  # Should open Streamlit demo in browser

🚨 Troubleshooting

Common Issues:

1. Python 3.11 not found

   • Install from python.org
   • On Ubuntu: sudo apt install python3.11 python3.11-venv
   • On macOS with Homebrew: brew install python@3.11

2. Permission errors on Windows

   • Run terminal as Administrator
   • Or use: python -m pip install --user -r requirements/dev.txt

3. Conda environment conflicts

   • Remove existing environment: conda env remove -n sharepoint-ai
   • Create fresh environment

4. UV installation issues

   • Check UV documentation for platform-specific instructions
   • Alternative: Use pip in virtual environment

Docker Deployment

1. Build the Docker image

   docker build -t sharepoint-ai-assistant .

2. Run with Docker Compose

   docker-compose up -d

⚙️ Configuration

Environment Variables

Create a .env file in the root directory:

# SharePoint Configuration
SHAREPOINT_SITE_URL=https://yourcompany.sharepoint.com/sites/yoursite
SHAREPOINT_CLIENT_ID=your-client-id
SHAREPOINT_CLIENT_SECRET=your-client-secret

# LLM Configuration
LLM_MODEL=llama2
LLM_HOST=http://localhost:11434
LLM_TEMPERATURE=0.7
LLM_MAX_TOKENS=2048
LLM_TIMEOUT=30

# Logging Configuration
LOG_LEVEL=INFO
LOG_FORMAT=json
LOG_FILE=logs/app.log

# Security Configuration
ENABLE_RATE_LIMITING=true
MAX_REQUESTS_PER_MINUTE=60

SharePoint App Registration

1. Register an app in Azure AD

   • Go to Azure Portal > App registrations
   • Create a new registration
   • Note the Application (client) ID

2. Configure API permissions

   • Add SharePoint permissions:
     • Sites.Read.All
     • Sites.ReadWrite.All (if write access needed)

3. Create client secret

   • Go to Certificates & secrets
   • Create a new client secret
   • Copy the secret value

🧪 Testing

Run Unit Tests

pytest tests/unit/ -v

Run Tests with Coverage

pytest tests/ --cov=src --cov-report=html

Run Specific Test File

pytest tests/unit/test_validation.py -v

📊 Logging and Monitoring

Log Levels

• DEBUG: Detailed diagnostic information
• INFO: General application flow
• WARNING: Potentially harmful situations
• ERROR: Error events that allow application to continue
• CRITICAL: Serious errors that may abort the program

Log Locations

• Console: Real-time logging output
• File: logs/app.log (configurable)
• Structured: JSON format for log aggregation

Performance Monitoring

The application includes built-in performance monitoring:

• Function execution times
• Memory usage tracking
• API response times
• Error rate monitoring

🔒 Security Features

Input Validation

• XSS protection with HTML sanitization
• SQL injection prevention
• File type validation
• Size limit enforcement

Authentication

• Secure credential handling
• Environment-based configuration
• No hardcoded secrets

Rate Limiting

• Configurable request limits
• Protection against abuse
• Graceful degradation

🚀 Deployment

Production Checklist

• Environment variables configured
• SharePoint app registered and permissions granted
• Ollama service running (for local LLM)
• Log directory writable
• Health checks configured
• Monitoring set up

Docker Production Deployment

# Build production image
docker build -t sharepoint-ai-assistant:latest .

# Run with production settings
docker run -d \
  --name sharepoint-ai-assistant \
  -p 8501:8501 \
  --env-file .env \
  -v $(pwd)/logs:/app/logs \
  sharepoint-ai-assistant:latest

Kubernetes Deployment

apiVersion: apps/v1
kind: Deployment
metadata:
  name: sharepoint-ai-assistant
spec:
  replicas: 3
  selector:
    matchLabels:
      app: sharepoint-ai-assistant
  template:
    metadata:
      labels:
        app: sharepoint-ai-assistant
    spec:
      containers:
        - name: app
          image: sharepoint-ai-assistant:latest
          ports:
            - containerPort: 8501
          env:
            - name: SHAREPOINT_SITE_URL
              valueFrom:
                secretKeyRef:
                  name: sharepoint-secrets
                  key: site-url
          # Add other environment variables

🔧 Troubleshooting

Common Issues

1. SharePoint Connection Failed

   • Verify client ID and secret
   • Check app permissions in Azure AD
   • Ensure site URL is correct

2. LLM Service Unavailable

   • Verify Ollama is running
   • Check model is downloaded
   • Verify host URL and port

3. File Preview Errors

   • Check file size limits
   • Verify file type is supported
   • Ensure sufficient memory

Debug Mode

Enable debug logging:

export LOG_LEVEL=DEBUG
streamlit run main.py

Health Checks

The application provides health check endpoints:

• Streamlit Health: http://localhost:8501/_stcore/health
• Application Status: Check logs for service status

🤝 Contributing

Development Workflow

1. Fork the repository
2. Create a feature branch
3. Make changes with tests
4. Run the test suite
5. Submit a pull request

Code Standards

• Python: Follow PEP 8
• Type Hints: Use type annotations
• Documentation: Docstrings for all functions
• Testing: Unit tests for new features
• Logging: Appropriate log levels

Pre-commit Hooks

pip install pre-commit
pre-commit install

📄 License

This project is licensed under the MIT License - see the LICENSE file for details.

🆘 Support

Documentation

• SharePoint API Documentation
• Streamlit Documentation
• LangChain Documentation

Issues

Report issues on GitHub Issues

Community

• Discussions
• Wiki

🎯 Roadmap

Version 2.0 (Planned)

• Multi-tenant support
• Advanced search with filters
• Document summarization
• Workflow automation
• Mobile-responsive UI
• API endpoints
• Advanced analytics
• Integration with Teams

Version 1.1 (In Progress)

• Enhanced error handling
• Comprehensive logging
• Input validation
• Unit testing framework
• Docker optimization
• Performance improvements
• Additional file formats
• Caching layer

---

Built with ❤️ for the SharePoint community