Gen AI - RAG Application Development using LlamaIndex

🚀 Course Title: Gen AI - RAG Applications Development using LlamaIndex 🐫

🎉 Headline: Master RAG Application Development with OpenAI GPT, Google Gemini LLM & Vector Databases!

Dive into the World of RAG Applications with LlamaIndex and Gen AI!

🧠 Course Description:

Embark on a transformative learning journey with our comprehensive online course designed to equip you with the skills to build sophisticated RAG (Reason, Argue, and Generate) applications using OpenAI GPT and Google Gemini APIs, LlamaIndex LLM Framework, and advanced Vector Databases like ChromaDB and Pinecone.

Whether you're a software engineer, data scientist, or an AI enthusiast, this course will guide you through the intricacies of RAG application development with a focus on hands-on learning. We'll start with the fundamentals and gradually delve into advanced concepts such as Language Embeddings, QueryEngines, Retrievers, and Prompt Engineering techniques to enhance efficiency in your applications.

🛠️ Key Takeaways:

• Comprehensive Understanding: Gain a solid grasp of LLM RAG applications, including their components like Agents, Tools, QueryPipelines, Retrievers, and QueryEngines.
• Hands-on Experience: Engage with multiple projects that will solidify your understanding and practical skills in developing RAG applications.
• Real-world Applications: Learn how to apply your knowledge to real-world problems, enhancing your ability to solve complex business challenges with LLM technology.

👩‍💻 Projects & Hands-on Learning:

• Basic RAG: Chat with multiple PDF documents using a VectorStore and other components for an immersive learning experience.
• ReAct Agent: Build a Calculator using a ReAct Agent, showcasing the practical application of LLM tools.
• Document Agent with Dynamic Tools: Create a Document Agent capable of dynamically creating QueryEngineTools and orchestrating queries through the Agent for complex interactions.
• Semantic Similarity: Explore Semantic Similarity operations to calculate similarity scores, essential for fine-tuning RAG applications.
• Sequential Query Pipeline: Construct a Simple Sequential Query Pipeline to understand the flow of data and interactions in RAG systems.
• DAG Pipeline: Develop complex Directed Acyclic Graph (DAG) Pipelines to manage intricate workflows within RAG applications.
• Dataframe Pipeline: Work with Dataframe Analysis Pipelines, including Pandas Output Parsers and Response Synthesizers for data manipulation and interpretation.
• Working with SQL Databases: Create SQL Database ingestion bots using various approaches to integrate structured data sources.

🔥 Learning Outcomes:

For each project, you will:

• Understand the business problem at hand.
• Identify which LLM components from LlamaIndex are essential for the solution.
• Analyze the outcomes of your work.
• Discover additional use cases that can be solved using a similar approach.

Join us on this exciting journey to master RAG application development with cutting-edge AI technologies! 🌟

Enroll Now and Transform Your AI Development Skills with LlamaIndex and Gen AI! 🚀💻✨