Advance RAG : Vector to Graph RAG Neo4j Adaptive AutoGen AWS

Unlock the Power of Retrieval-Augmented Generation (RAG): Build Intelligent AI Applications

Dive into the exciting world of Retrieval-Augmented Generation (RAG), a revolutionary AI technique transforming how we build intelligent applications. This course equips developers, data scientists, AI enthusiasts, and quality engineers with the skills to harness the combined power of retrieval and generative models. Whether you're a beginner or looking to advance your AI expertise, you'll learn to create practical RAG applications, from simple vector RAG chatbots to sophisticated systems leveraging Graph RAG and Self-Reflective RAG.

What you'll learn:

• RAG Fundamentals: Grasp the core concepts of RAG and Natural Language Processing (NLP).
• NLP Essentials: Understand key NLP techniques with practical examples, including:
  • Tokenization: Breaking down text into meaningful units.
  • Chunking: Dividing text into manageable segments.
  • TF-IDF: Understanding term frequency-inverse document frequency.
  • Embeddings: Representing text as numerical vectors.
• NLP Model Evaluation: Learn how to assess NLP models, from traditional rule-based systems to advanced transformer models.
• Transformer Models: Explore the inner workings of transformer models and their key components.
• Hands-on Implementation: Set up your development environment for practical RAG implementation.
• Chatbot Development: Build your first interactive chatbot using Streamlit and LangChain.
• Vector RAG with Groq API: Implement a high-performance vector RAG chatbot leveraging the power of the Groq API.
• Graph RAG: Explore and implement Graph RAG using Neo4j for enhanced knowledge retrieval.
• Self-Reflective RAG: Learn and implement Self-Reflective (Adaptive) RAG with LangGraph to improve accuracy and relevance.
• Real-World RAG Use Cases: Discover practical applications of RAG across various industries.
• Re-ranking Techniques: Optimize RAG systems with advanced re-ranking methods.
• Agentic RAG: Explore Agentic RAG and AutoGen RAG for more complex and autonomous applications.
• Cloud Integration: Create RAG applications using AWS Bedrock Knowledge Base, BOTO3, Streamlit, Lambda, and S3.
• Reinforcement: Test your knowledge with engaging quizzes.

This course provides a comprehensive journey into RAG, covering both theoretical foundations and practical implementation. You'll gain the skills to build powerful, context-aware AI applications that deliver accurate and relevant results. By the end, you'll be proficient in using tools like Streamlit, LangChain, LangGraph, and the Groq API to create cutting-edge RAG-based solutions.