Gen AI - LLM RAG Two in One - LangChain + LlamaIndex

Unlock the power of Large Language Models (LLMs) with this comprehensive course on building advanced LLM RAG (Retrieval-Augmented Generation) applications! You'll gain hands-on expertise using leading frameworks like LangChain and LlamaIndex, alongside powerful APIs from OpenAI GPT and Google Gemini, and robust Vector Databases such as ChromaDB and Pinecone.

This course offers a deep dive into the core components of LLM RAG apps, covering everything from Agents, Tools, and Chains to Memory, QueryPipelines, Retrievers, and Query Engines. You'll learn how to effectively utilize Language Embeddings and Vector Databases to create efficient semantic search and similarity-based RAG applications. Master essential Prompt Engineering techniques to optimize the performance of your RAG systems.

Here's what you'll build:

• Conversational Memory Chatbot: Develop a chatbot that remembers past conversations using downloaded web data and a Vector Database.
• CV Upload & Semantic CV Search App: Create an application to upload and intelligently search through resumes (CVs) using semantic understanding.
• Invoice Extraction RAG App: Build an application to extract information from invoices using RAG.
• Structured Data Analytics App: Develop an application that answers natural language questions based on structured data.
• ReAct Agent Calculator: Create a calculator application powered by a ReAct Agent and various tools.
• Document Agent with Dynamic Tools: Build a document processing agent capable of dynamically using different query tools.
• Sequential Query Pipeline: Develop simple, sequential query pipelines for data retrieval.
• DAG Pipeline: Create complex Directed Acyclic Graph (DAG) pipelines for sophisticated data processing.
• Dataframe Pipeline: Develop advanced data analysis pipelines using Pandas, including output parsing and response synthesis.
• SQL Database Integration: Build a bot to ingest data from SQL databases.
• FAST API Deployment: Learn to deploy your LangChain application using FAST API, mirroring a live deployment environment.

This twin-framework approach – leveraging both LangChain and LlamaIndex – provides a broader understanding of RAG development, empowering you to choose the best tools for your projects and maximize their potential. Whether you're a developer, data scientist, or AI enthusiast, this course will equip you with the skills to build cutting-edge LLM applications.