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

This course leverages the power of both LangChain and Llama Index frameworks, along with OpenAI GPT and Google Gemini AP Is, and Vector Databases like ChromaDB and Pinecone. It is designed to provide you with a comprehensive understanding of building advanced LLM RAG applications through in-depth conceptual learning and hands-on sessions. The course covers essential aspects of LLM RAG apps, exploring components from both frameworks such as Agents, Tools, Chains, Memory, Query Pipelines, Retrievers, and Query Engines in a clear and concise manner. You'll also delve into Language Embeddings and Vector Databases, enabling you to develop efficient semantic search and similarity-based RAG applications. Additionally, the course covers various Prompt Engineering techniques to enhance the efficiency of your RAG applications.List of Projects/Hands-on included: Develop a Conversational Memory Chatbot using downloaded web data and Vector DB Create a CV Upload and Semantic CV Search App Invoice Extraction RAG App Create a Structured Data Analytics App that uses Natural Language Queries Re Act Agent: Create a Calculator App using a Re Act Agent and Tools Document Agent with Dynamic Tools: Create multiple Query Engine Tools dynamically and orchestrate queries through Agents Sequential Query Pipeline: Create Simple Sequential Query PipelinesDAG Pipeline: Develop complex DAG Pipelines Dataframe Pipeline: Develop complex Dataframe Analysis Pipelines with Pandas Output Parser and Response Synthesizer Working with SQL Databases: Develop SQL Database ingestion Bot Create a FAST API for your LangChain Application just