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Agentic AI Systems with Vector DBs & RAG · LearnSpace
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Agentic AI Systems with Vector DBs & RAG

Курс от LearnKartS
Продвинутый≈ 15.2 чАнглийский
О курсеНавыкиПрограммаПреподаватели

О курсе

Want to build AI that learns, adapts, and evolves in real-time? The future of AI is Agentic AI. While traditional models are built on static logic, Agentic AI enables systems to learn, think, and make autonomous decisions, evolving with each interaction and adapting to new data. In this Agentic AI course, you’ll master RAG (Retrieval-Augmented Generation) and vector databases like pgVector and ChromaDB, unlocking the ability to build AI systems that dynamically retrieve data, learn from context, and make smarter decisions. You’ll gain hands-on experience creating scalable, real-time adaptive AI—critical for industries looking to stay ahead. What makes this course unique? Unlike others, it combines cutting-edge RAG tools with practical applications of pgVector and ChromaDB, giving you the power to create AI that evolves with every interaction. Perfect for AI developers, engineers, and innovators eager to shape the future of AI. Don’t just follow trends—lead them. Enroll now and start building the future of AI today!

Навыки, которые вы освоите

Vector DatabasesMongoDBNode.JSRetrieval-Augmented GenerationPostgreSQLEmbeddingsAgentic systemsDatabase ManagementAPI DesignAgentic WorkflowsAI WorkflowsData IntegrationModel Context ProtocolData StorageSoftware InstallationData StructuresGenerative AI AgentsData TransformationScalability

Программа курса

3 модулей · 95 учебных материалов

01Agentic Systems and AI Memory33 материалов

Agentic Systems and AI Memory

Course IntroductionВидеоWhat is an Agentic System?ВидеоThe "Why" - Beyond Simple Chatbots (Why Agentic)ВидеоThe Decision Loop - Why Hard-Coded Logic FailsВидеоThe Agentic Design Pattern - Passing RAG as a Tool

Учитесь у экспертов

LearnKartS

Преподаватель курса

Nikhil Agarwal

Преподаватель курса

Agentic AI Systems with Vector DBs & RAG
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Обучение откроется на Coursera
в новой вкладке

Обучение на Coursera

≈ 15.2 ч

3 модулей

Язык: Английский

Часть программы вашего университета
Видео
Checkpoint QuizЗадание
Proficiency QuizЗадание

Vector Databases and the AI Memory Layer

Architecture Battle: RAG vs. MCP vs. AgentsВидеоThe Ecosystem: Apps that Support MCPВидеоThe Memory Problem: Why AI Needs Vector Databases?ВидеоWhat Vector Databases Do?ВидеоWhat is ChromaDB?ВидеоWhat is pgVector?ВидеоCheckpoint QuizЗаданиеProficiency QuizЗадание

pgVector, ChromaDB, and Static RAG Assets

ChromaDB vs pgVector - When to Choose?ВидеоPreparing File Structure and Data Assets (PDFs/Docs) for RAGВидеоConfiguring ChromaDB in Local EnvironmentВидеоSetting up ChromaDB in NodeJS & Environment ConfigurationВидеоCreating Vector Store & Upsert FunctionВидеоSearch Function & Data PreparationВидеоCheckpoint QuizЗаданиеProficiency QuizЗадание

Building Static RAG Knowledge Base and Chat Integration

Vector Store Backend Selection & Final SetupВидеоIntroduction to Static RAG and Creating the Knowledge BaseВидеоCreating rag.js and Reading the Knowledge Base FileВидеоPreparing Context from JSON Data (Mapping Questions & Answers)ВидеоBuilding the Prompt with Context and User QueryВидеоIntegrating RAG in Chat Router and Testing the Q&A BotВидеоCheckpoint QuizЗаданиеAgentic AI with Vector Databases: Static RAG Setup, Context Engineering & Chat IntegrationDIALOGUEProficiency QuizЗаданиеBuilding a RAG-Based Q&A System with Vector DatabasesDIALOGUE
02PGVector and RAG Engine Development31 материалов

PostgreSQL and pgVector Implementation

Installing PostgreSQL, PG, and PGVectorВидеоConfiguring PG Admin, Servers, and Query ToolВидеоConfiguring PostgreSQL & pgVector for WindowsВидеоEnabling the pgVector Extension for Database ConfigurationВидеоConnecting NodeJS App to pgVectorВидеоCheckpoint QuizЗаданиеProficiency QuizЗадание

Building the pgVector Vector Store and Search Logic

Setting Up PostgreSQL Pool and PGVector ConnectionВидеоCreating the VectorStorePG Class and Initialization LogicВидеоBuilding the Upsert Function for Insert and Update OperationsВидеоImplementing the Vector Search Query Using SQLВидеоTesting PGVector Ingestion and Verifying Data in PGAdminВидеоCheckpoint QuizЗадание

RAG Tooling and Agentic Workflow Testing

Preparing the RAG Engine Interface & ClassВидеоGenerating Query Embeddings & Validating DimensionsВидеоSearching Vector DB, Building Context & PromptВидеоCreating and Configuring the RAG Tool in MCPВидеоRegistering the RAG Tool and Updating the AI System Prompt ВидеоCheckpoint QuizЗадание

Testing RAG Engine and Advanced Prompt Engineering

Environment Setup and Starting Services for System TestingВидеоTesting Tools and Queries in the Agentic WorkflowВидеоRAG Testing with PGVector and Debugging ChromaDBВидеоPrompt Engineering and Advanced Query TestingВидеоConfiguring OpenAI Testing and Updating RAG Prompt LogicВидеоTesting Agentic AI with GPT, Tool Calls, and Vector Database SwitchingВидео
03MongoDB Integration and Schema Optimization31 материалов

MongoDB Integration in the Agentic Stack

MongoDB Architecture Overview and Integration StrategyВидеоInstalling and Configuring MongoDB Locally on macOSВидеоInstalling and Configuring MongoDB Locally on WindowsВидеоConfiguring MongoDB Environment and Installing MongooseВидеоCheckpoint QuizЗаданиеProficiency QuizЗадание

MongoDB Connection Schema and Data Management

Implementing MongoDB Connection and Starting the ServerВидеоSchema & Customer InterfaceВидеоSchema Constraints & Model SetupВидеоInsert Data via MCPВидеоError Handling & Future StepsВидеоRefactoring Customer Services: Mock Data → MongoDB & Fetching Latest CustomersВидео

Orders, Schema Optimization, and Query Performance

Fetching Customers by ID & Understanding MongoDB SchemasВидеоRefactoring Orders: Populating Customer Data & Preparing Order InsertionВидеоCreating & Testing Orders in MCP Server and Chat ApplicationВидеоMongoDB Data Issue: Understanding _id vs id and Why Fetching FailsВидеоCreating Global Schema Options and Removing __v from MongoDB DocumentsВидеоCheckpoint QuizЗадание

Optimizing Schema and API Data Handling

Implementing Transform Function to Clean API ResponseВидеоUsing Getters to Format Dates and Improve Schema Data HandlingВидеоApplying Global Schema Options to Customer Model and Testing ResultsВидеоOptimizing Order Schema with ObjectId References and PopulateВидеоFixing Tool Output Types and Fetching Orders with Customer DetailsВидеоCheckpoint QuizЗадание
Proficiency QuizЗадание
Proficiency QuizЗадание
Checkpoint QuizЗадание
Agentic AI with Vector Databases: Testing, Debugging & Advanced RAG Workflow OptimizationDIALOGUE
Proficiency QuizЗадание
Testing and Optimizing an Agentic RAG System with Vector DatabasesDIALOGUE
Checkpoint QuizЗадание
Proficiency QuizЗадание
Agentic AI with Vector Databases: MongoDB Integration, Schema Design & Data Handling in Agentic SystemsDIALOGUE
Proficiency QuizЗадание
Managing MongoDB Data and Integrating Orders in an Agentic AI SystemDIALOGUE
Proficiency QuizЗадание
Course SummaryВидео