RAG-Toolkit: Comprehensive Guide to RAG Implementations

This repository provides a comprehensive collection of Retrieval-Augmented Generation (RAG) implementations using various modern frameworks and tools. Each implementation demonstrates different approaches and capabilities for building robust RAG solutions.

🚀 Implementations

1. Verba Implementation

• Tech Stack: Verba, Weaviate, OpenAI
• Key Features:
  • PDF document ingestion and processing
  • Document chunking with overlap
  • Vector storage using Weaviate
  • Conversational retrieval using GPT models
  • Source attribution for answers
• Best For: Document-heavy applications requiring precise retrieval and attribution

2. LangChain Implementation

• Tech Stack: LangChain, ChromaDB, OpenAI
• Key Features:
  • Multiple PDF document processing
  • Automatic text chunking
  • Semantic search using ChromaDB
  • Conversational QA interface
  • Chat history support
• Best For: Complex RAG pipelines with multiple components

3. LlamaIndex Implementation

• Tech Stack: LlamaIndex, OpenAI, Vector Store
• Key Features:
  • Advanced data connectors
  • Structured data handling
  • Optimized query engines
  • Custom data indexing
• Best For: Data-intensive applications with diverse sources

4. Phoenix Implementation

• Tech Stack: Phoenix, Vector DB
• Key Features:
  • Real-time vector indexing
  • High-performance retrieval
  • Scalable architecture
  • Modern deployment options
• Best For: High-performance, scalable RAG systems

5. MongoDB Implementation

• Tech Stack: MongoDB Atlas, Vector Search
• Key Features:
  • Atlas Vector Search integration
  • Enterprise-grade security
  • Scalable document storage
  • Native vector indexing
• Best For: Enterprise applications requiring robust data management

6. Haystack Implementation

• Tech Stack: Haystack Framework
• Key Features:
  • Modular pipeline architecture
  • Multiple retriever options
  • Production-ready components
  • Flexible deployment
• Best For: Production-grade search and QA systems

7. NeMo Guardrails Implementation

• Tech Stack: NeMo Guardrails, LLM
• Key Features:
  • Content filtering
  • Topic boundaries
  • Conversation flow control
  • Safety mechanisms
• Best For: Applications requiring controlled AI interactions

🛠 Getting Started

Each implementation directory contains:

• Detailed README with setup instructions
• Complete source code
• Configuration examples
• Usage demonstrations
• Testing scripts

Prerequisites

• Python 3.9+
• Conda (for environment management)
• Relevant API keys (OpenAI, etc.)
• Vector store setup (varies by implementation)

General Setup Steps

1. Clone the repository:

git clone <repository-url>
cd RAG-Toolkit

2. Choose an implementation directory
3. Follow the specific README instructions
4. Set up required API keys and services
5. Run the example code

📊 Comparison Matrix

Implementation	Vector Store	LLM Support	Document Types	Deployment Complexity
Verba	Weaviate	OpenAI	PDF	Medium
LangChain	ChromaDB	Multiple	Multiple	Low
LlamaIndex	Multiple	Multiple	Multiple	Medium
Phoenix	Custom Vector DB	Multiple	Multiple	High
MongoDB	Atlas Search	Multiple	Multiple	Medium
Haystack	Multiple	Multiple	Multiple	Medium
NeMo	-	Multiple	Text	Low

🤝 Contributing

Contributions are welcome! To contribute:

1. Fork the repository
2. Create a feature branch
3. Commit your changes
4. Push to the branch
5. Open a Pull Request

📝 License

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

🔗 Additional Resources

• Verba Documentation
• LangChain Documentation
• LlamaIndex Documentation
• MongoDB Atlas Documentation