banko-ai-assistant 1.0.4

AI-powered expense analysis and RAG system with CockroachDB vector search and multi-provider AI support

🤖 Banko AI Assistant - RAG Demo

A modern AI-powered expense analysis application with Retrieval-Augmented Generation (RAG) capabilities, built with CockroachDB vector search and multiple AI provider support.

✨ Features

• 🔍 Advanced Vector Search: Enhanced expense search using CockroachDB vector indexes
• 🤖 Multi-AI Provider Support: OpenAI, AWS Bedrock, IBM Watsonx, Google Gemini
• 🔄 Dynamic Model Switching: Switch between models without restarting the app
• 👤 User-Specific Indexing: User-based vector indexes with regional partitioning
• 📊 Data Enrichment: Contextual expense descriptions for better search accuracy
• 💾 Intelligent Caching: Multi-layer caching system for optimal performance
• 🌐 Modern Web Interface: Clean, responsive UI with real-time chat
• 📈 Analytics Dashboard: Comprehensive expense analysis and insights
• 📦 PyPI Package: Easy installation with pip install banko-ai-assistant
• 🎯 Enhanced Context: Merchant and amount information included in search context
• ⚡ Performance Optimized: User-specific vector indexes for faster queries

🚀 Quick Start

Prerequisites

• Python 3.8+
• CockroachDB (running locally or cloud)
• AI Provider API Key (OpenAI, AWS, IBM Watsonx, or Google Gemini)

Installation

Option 1: PyPI Installation (Recommended)

# Install from PyPI (when published)
pip install banko-ai-assistant

# Run the application
banko-ai run

Option 2: Development Installation

# Clone the repository
git clone https://github.com/cockroachlabs-field/banko-ai-assistant-rag-demo
cd banko-ai-assistant-rag-demo

# Install the package in development mode
pip install -e .

# Run the application
banko-ai run

Option 3: Direct Dependencies

# Install dependencies directly
pip install -r requirements.txt

# Run the original app.py (legacy method)
python app.py

Configuration

Set up your environment variables:

# Required: Database connection
export DATABASE_URL="cockroachdb://root@localhost:26257/banko_ai?sslmode=disable"

# Required: AI Service (choose one)
export AI_SERVICE="watsonx"  # or "openai", "aws", "gemini"

# AI Provider Configuration (choose based on AI_SERVICE)
# For IBM Watsonx:
export WATSONX_API_KEY="your_api_key_here"
export WATSONX_PROJECT_ID="your_project_id_here"
export WATSONX_MODEL="meta-llama/llama-2-70b-chat"

# For OpenAI:
export OPENAI_API_KEY="your_api_key_here"
export OPENAI_MODEL="gpt-3.5-turbo"

# For AWS Bedrock:
export AWS_ACCESS_KEY_ID="your_access_key"
export AWS_SECRET_ACCESS_KEY="your_secret_key"
export AWS_REGION="us-east-1"
export AWS_MODEL="anthropic.claude-3-sonnet-20240229-v1:0"

# For Google Gemini:
export GOOGLE_APPLICATION_CREDENTIALS="path/to/service-account.json"
export GOOGLE_MODEL="gemini-1.5-pro"

Running the Application

The application automatically creates database tables and loads sample data (5000 records by default):

# Start with default settings (5000 sample records)
banko-ai run

# Start with custom data amount
banko-ai run --generate-data 10000

# Start without generating data
banko-ai run --no-data

# Start with debug mode
banko-ai run --debug

🎯 What Happens on Startup

1. Database Connection: Connects to CockroachDB and creates necessary tables
2. Table Creation: Creates expenses table with vector indexes and cache tables
3. Data Generation: Automatically generates 5000 sample expense records with enriched descriptions
4. AI Provider Setup: Initializes the selected AI provider and loads available models
5. Web Server: Starts the Flask application on http://localhost:5000

📊 Sample Data Features

The generated sample data includes:

• Rich Descriptions: "Bought food delivery at McDonald's for $56.68 fast significant purchase restaurant and service paid with debit card this month"
• Merchant Information: Realistic merchant names and categories
• Amount Context: Expense amounts with contextual descriptions
• Temporal Context: Recent, this week, this month, etc.
• Payment Methods: Bank Transfer, Debit Card, Credit Card, Cash, Check
• User-Specific Data: Multiple user IDs for testing user-specific search

🌐 Web Interface

Access the application at http://localhost:5000

Main Features

• 🏠 Home: Overview dashboard with expense statistics
• 💬 Chat: AI-powered expense analysis and Q&A
• 🔍 Search: Vector-based expense search
• ⚙️ Settings: AI provider and model configuration
• 📊 Analytics: Detailed expense analysis and insights

🔧 CLI Commands

# Run the application
banko-ai run [OPTIONS]

# Generate sample data
banko-ai generate-data --count 2000

# Clear all data
banko-ai clear-data

# Check application status
banko-ai status

# Search expenses
banko-ai search "food delivery" --limit 10

# Show help
banko-ai help

🔌 API Endpoints

Endpoint	Method	Description
/	GET	Web interface
/api/health	GET	System health check
/api/ai-providers	GET	Available AI providers
/api/models	GET	Available models for current provider
/api/search	POST	Vector search expenses
/api/rag	POST	RAG-based Q&A

API Examples

# Health check
curl http://localhost:5000/api/health

# Search expenses
curl -X POST http://localhost:5000/api/search \
  -H "Content-Type: application/json" \
  -d '{"query": "food delivery", "limit": 5}'

# RAG query
curl -X POST http://localhost:5000/api/rag \
  -H "Content-Type: application/json" \
  -d '{"query": "What are my biggest expenses this month?", "limit": 5}'

🏗️ Architecture

Database Schema

• expenses: Main expense table with vector embeddings
• query_cache: Cached search results
• embedding_cache: Cached embeddings
• insights_cache: Cached AI insights
• vector_search_cache: Cached vector search results
• cache_stats: Cache performance statistics

Vector Indexes

-- User-specific vector index for personalized search
CREATE INDEX idx_expenses_user_embedding ON expenses 
USING cspann (user_id, embedding vector_l2_ops);

-- General vector index for global search
CREATE INDEX idx_expenses_embedding ON expenses 
USING cspann (embedding vector_l2_ops);

-- Note: Regional partitioning syntax may vary by CockroachDB version
-- CREATE INDEX idx_expenses_regional ON expenses 
-- USING cspann (user_id, embedding vector_l2_ops) 
-- LOCALITY REGIONAL BY ROW AS region;

Benefits:

• User-specific queries: Faster search within user's data
• Contextual results: Enhanced merchant and amount information
• Scalable performance: Optimized for large datasets
• Multi-tenant support: Isolated user data with shared infrastructure

🔄 AI Provider Switching

Switch between AI providers and models dynamically:

1. Go to Settings in the web interface
2. Select your preferred AI provider
3. Choose from available models
4. Changes take effect immediately

Supported Providers

• OpenAI: GPT-3.5, GPT-4, GPT-4 Turbo
• AWS Bedrock: Claude 3 Sonnet, Claude 3 Haiku, Llama 2
• IBM Watsonx: Granite models, Llama 2, Mistral
• Google Gemini: Gemini 1.5 Pro, Gemini 1.5 Flash

📈 Performance Features

Caching System

• Query Caching: Caches search results for faster responses
• Embedding Caching: Caches vector embeddings to avoid recomputation
• Insights Caching: Caches AI-generated insights
• Multi-layer Optimization: Intelligent cache invalidation and refresh

Vector Search Optimization

• User-Specific Indexes: Faster search for individual users
• Regional Partitioning: Optimized for multi-region deployments
• Data Enrichment: Enhanced descriptions improve search accuracy
• Batch Processing: Efficient data loading and processing

Advanced Vector Features

For detailed demonstrations of vector indexing and search capabilities:

📖 Vector Index Demo Guide - Comprehensive guide covering:

• User-specific vector indexing
• Regional partitioning with multi-region CockroachDB
• Performance benchmarking
• Advanced search queries
• RAG with user context
• Troubleshooting and best practices

🛠️ Development

Project Structure

banko_ai/
├── ai_providers/          # AI provider implementations
├── config/               # Configuration management
├── static/               # Web assets and images
├── templates/            # HTML templates
├── utils/                # Database and cache utilities
├── vector_search/        # Vector search and data generation
└── web/                  # Flask web application

Adding New AI Providers

1. Create a new provider class in ai_providers/
2. Extend the BaseAIProvider class
3. Implement required methods
4. Add to the factory in ai_providers/factory.py

🐛 Troubleshooting

Common Issues

Database Connection Error

# Check CockroachDB is running
cockroach start --insecure --listen-addr=localhost:26257

# Verify database exists
cockroach sql --url="postgresql://root@localhost:26257/banko_ai?sslmode=disable" --execute "SHOW TABLES;"

AI Provider Disconnected

• Verify API keys are set correctly
• Check network connectivity
• Ensure the selected model is available

No Search Results

• Ensure sample data is loaded: banko-ai generate-data --count 1000
• Check vector indexes are created
• Verify search query format

Debug Mode

# Run with debug logging
banko-ai run --debug

# Check application status
banko-ai status

📝 License

MIT License - see LICENSE file for details.

🤝 Contributing

1. Fork the repository
2. Create a feature branch
3. Make your changes
4. Add tests if applicable
5. Submit a pull request

📞 Support

For issues and questions:

• Check the troubleshooting section
• Review the API documentation
• See the Vector Index Demo Guide for advanced features
• Open an issue on GitHub

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Built with ❤️ using CockroachDB, Flask, and modern AI technologies