Learn PostgreSQL Offline vs Learn LangChain

PostgreSQL is a popular relational database management system (RDBMS). Our PostgreSQL tutorial provides basic and advanced concepts of PostgreSQL. In this tutorial, we are going to learn all the topics of PostgreSQL language, such as create a database, drop a database, select database, select table, update a record, create a table, delete record, drop table, triggers, functions, insert the record, procedures, cursors, etc. - What is PostgreSQL Database? PostgreSQL is an ORDBMS [Open-Source Object-Relational Database Management System]. It is used to store data securely; supporting best practices, and allow recovering them when the request is processed. - History of PostgreSQL PostgreSQL was developed by the PostgreSQL Global Development Group, which is led by a computer science professor named Michael Stonebraker at the University of California, Berkeley [UCB]. The professor's stonebreaker started in 1986 as a follow-up project and a post-Ingres project to overcome the problems of the existing database system. Initially, it was called as Postgres, but in 1996, the project was renamed to PostgreSQL. Then again, in 2007, they did some analysis, and the development team decided to preserve the name as PostgreSQL. And now, it is the most innovative open-source database available in the market.

Learn LangChain and build modern AI applications with practical, structured lessons.Learn how large language model apps work—from prompt templates and chat models to chains, agents, retrieval-augmented generation, and production deployment. Whether you are beginning with LangChain or expanding your LLM engineering skills, Learn LangChain helps you study the concepts and patterns used to build real AI products. WHAT YOU’LL LEARN - LangChain fundamentals and setup - LLMs, chat models, prompts, and structured output - LCEL and composable application chains - Memory, conversation workflows, and state - Document loaders, text splitters, embeddings, and vector stores - Retrieval-augmented generation, or RAG - Advanced RAG patterns, including Graph RAG and Agentic RAG - AI agents, tools, ReAct workflows, and multi-agent systems - LangGraph concepts, nodes, state, and workflows - LangSmith, LangServe, templates, observability, and debugging - Vector databases, semantic search, and retrieval pipelines - AI evaluation, testing, quality measurement, and RAG evaluation - Streaming, caching, error handling, scaling, and production architecture - LoRA and efficient LLM fine-tuning - vLLM, model serving, inference, and deployment concepts - CrewAI and modern multi-agent application patterns - MCP concepts and connected AI workflows BUILD HANDS-ON PROJECTS Apply what you learn through practical projects, including: - Q&A bots with RAG - Multi-document research agents - AI customer support systems - Vector search and retrieval pipelines - Tool-using AI agents - Advanced multi-agent systems - Real-time RAG workflows - Production-oriented LLM applications LEARNING FEATURES - Bite-sized lessons that build from fundamentals to advanced topics - Python examples with JavaScript support - Quizzes, challenges, tips, and good-to-know explanations - Hands-on project-based learning - Progressive learning paths for beginners and experienced developers - Cheat sheets for LangChain fundamentals, RAG, and agents - Progress tracking to help you continue your learning journey Start your LangChain Journey Today !