🧠 Features Overview¶

RAG Modulo provides a comprehensive set of features for building production-ready Retrieval-Augmented Generation applications.

🎯 Core Features¶

🧠 Advanced AI Capabilities¶

🔍 Search & Retrieval¶

🎨 User Interface & Experience¶

🏗️ Architecture & Scalability¶

🚀 Advanced Features¶

🧠 Chain of Thought Reasoning¶

RAG Modulo includes advanced reasoning capabilities that break down complex problems into step-by-step solutions.

Key Benefits:

• ✅ Transparent Reasoning: See how the AI arrives at answers
• ✅ Token Breakdown: Detailed cost analysis for each reasoning step
• ✅ Debugging: Easier to identify and fix reasoning errors
• ✅ Trust: Increased confidence in AI-generated responses

Learn more about Chain of Thought →

📊 Token Tracking & Monitoring¶

Comprehensive token usage monitoring with intelligent warnings and analytics.

Features:

• ✅ Real-time Tracking: Monitor token usage across all conversations
• ✅ Usage Analytics: Detailed reports on token consumption
• ✅ Intelligent Warnings: Alerts when approaching token limits
• ✅ Cost Optimization: Identify opportunities to reduce token usage

Learn more about Token Tracking →

🔍 Intelligent Search & Retrieval¶

Advanced search capabilities with multiple strategies and optimizations.

Features:

• ✅ Hybrid Search: Combines semantic and keyword search
• ✅ Relevance Scoring: Intelligent ranking of search results
• ✅ Contextual Retrieval: Retrieves relevant context for queries
• ✅ Source Attribution: Tracks and cites information sources

Learn more about Search & Retrieval →

📄 Document Processing¶

Comprehensive document processing with support for multiple formats.

Supported Formats:

• ✅ PDF: Text, tables, and image extraction
• ✅ DOCX: Paragraph and formatting preservation
• ✅ TXT: Plain text processing
• ✅ XLSX: Spreadsheet data extraction

Processing Features:

• ✅ Intelligent Chunking: Optimal text segmentation
• ✅ Metadata Extraction: Automatic metadata generation
• ✅ Content Preservation: Maintains document structure
• ✅ Batch Processing: Efficient handling of large document sets

Learn more about Document Processing →

🔧 Integration Features¶

🤖 LLM Provider Support¶

Seamless integration with multiple Large Language Model providers.

Supported Providers:

• ✅ WatsonX: IBM's enterprise AI platform
• ✅ OpenAI: GPT models and embeddings
• ✅ Anthropic: Claude models
• ✅ Custom Providers: Easy integration of new providers

Features:

• ✅ Runtime Switching: Change providers without restart
• ✅ Load Balancing: Distribute requests across providers
• ✅ Fallback Support: Automatic failover to backup providers
• ✅ Cost Optimization: Choose providers based on cost and performance

Learn more about LLM Integration →

🗄️ Vector Database Support¶

Support for multiple vector database backends.

Supported Databases:

• ✅ Milvus: High-performance vector database
• ✅ Elasticsearch: Full-text search with vector support
• ✅ Pinecone: Managed vector database service
• ✅ Weaviate: Open-source vector database
• ✅ ChromaDB: Lightweight vector database

Features:

• ✅ Easy Migration: Switch between databases
• ✅ Performance Tuning: Optimized for each database
• ✅ Scalability: Horizontal scaling support
• ✅ Backup & Recovery: Data persistence and recovery

Learn more about Vector Databases →

🎯 Use Cases¶

📚 Knowledge Management¶

Perfect for:

• Corporate knowledge bases
• Technical documentation
• Research papers
• Legal documents
• Customer support

Benefits:

• ✅ Instant Answers: Find information quickly
• ✅ Contextual Responses: Answers based on relevant context
• ✅ Source Citations: Always know where information comes from
• ✅ Multi-format Support: Handle various document types

🤖 Customer Support¶

Perfect for:

• Automated customer service
• FAQ systems
• Product support
• Technical assistance
• Chatbots

Benefits:

• ✅ 24/7 Availability: Always-on customer support
• ✅ Consistent Responses: Standardized answers
• ✅ Escalation Support: Hand off to human agents
• ✅ Learning: Improve from interactions

🔬 Research & Analysis¶

Perfect for:

• Academic research
• Market analysis
• Competitive intelligence
• Data analysis
• Report generation

Benefits:

• ✅ Comprehensive Search: Find relevant information across sources
• ✅ Reasoning: Step-by-step analysis
• ✅ Citation: Proper source attribution
• ✅ Collaboration: Share insights with teams

🚀 Getting Started¶

Ready to explore these features? Here's how to get started:

1. Quick Start¶

# Clone and start RAG Modulo
git clone https://github.com/manavgup/rag-modulo.git
cd rag-modulo
make run-ghcr

2. Explore Features¶

• 🧠 Chain of Thought - Advanced reasoning
• 📊 Token Tracking - Usage monitoring
• 🔍 Search & Retrieval - Intelligent search
• ⚡ Cross-Encoder Reranking - 250x faster reranking
• 📄 Document Processing - Document handling
• 🤖 LLM Integration - Model providers
• 🎙️ Podcast Generation - AI-powered podcasts from documents

3. Try Examples¶

• 🚀 Getting Started - Quick start guide
• 🖥️ CLI Examples - Command-line examples
• 📚 API Examples - API usage examples

💡 Best Practices¶

🎯 Feature Selection¶

• Start Simple: Begin with basic search and retrieval
• Add Complexity: Gradually introduce advanced features
• Monitor Performance: Use token tracking to optimize costs
• Iterate: Continuously improve based on usage patterns

🔧 Configuration¶

• Choose Right Provider: Select LLM provider based on needs
• Optimize Chunking: Tune chunk size for your documents
• Monitor Usage: Track token consumption and costs
• Scale Gradually: Start small and scale as needed

📊 Monitoring¶

• Track Metrics: Monitor search quality and response time
• Analyze Usage: Understand how features are used
• Optimize Costs: Use token tracking to reduce expenses
• Improve Quality: Continuously enhance search results