Knowledge Bases & Memory for Agentic AI

  • IntermediateLevel

  • 800+Students Enrolled

  • 45 MinsDuration

  • 4.5Average Rating

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About this Course

  • Learn how to structure and store data in vector databases to enable intelligent information retrieval in AI agents.
  • Understand the role of knowledge bases and memory in Retrieval-Augmented Generation (RAG) and agentic workflows.
  • Build hands-on projects that show how agents use context, memory, and external knowledge to perform complex tasks.

Learning Outcomes

Build Smart Memory

Learn how agents store and retrieve data using vector memory

Master RAG Basics

TUnderstand how Retrieval-Augmented Generation powers agents

Create Knowledge Hubs

Build structured, searchable knowledge bases from raw data

Who Should Enroll

  • Professionals: Gain hands-on knowledge to build AI agents that leverage your organization's data
  • Aspiring Students: Explore how cutting-edge generative AI tools like RAG and AI agents work.
  • Data professionals interested in applying knowledge bases to enhance retrieval and reasoning in AI systems.

Course Curriculum

Explore a comprehensive curriculum covering Python, machine learning models, deep learning techniques, and AI applications.

tools

  1. 1. From Prompts to Purpose Evolving LLM Usage

  2. 2. Powering RAG with Vector Databases

  3. 3. Powering RAG with Vector Databases

  4. 4. RAG to Agents Building Autonomous AI Systems

  1. 1. Multi-Tool Agents in Action Solving Real-World Scenarios

  2. 2. Open-Source Agent Building with CrewAI

  3. 3. Designing Collaborative Multi-Agent Systems

Meet the instructor

Our instructor and mentors carry years of experience in data industry

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Tuana Çelik

DevRel & AI Engineering

Tuana Çelik is a Developer Relations Engineer at Weaviate. In her role, she educates the open-source community about AI tools, the latest methods, and workflows.

company logo
JP Hwang

Developer Educator

JP Hwang is passionate about empowering others to build with AI. He brings a combination of technical expertise, empathy, and bad jokes to all his endeavors to make learning fun and empowering for both sides.

Get this Course Now

With this course you’ll get

  • 45 Mins

    Duration

  • Tuana Çelik, JP Hwang

    Instructor

  • Intermediate

    Level

Certificate of completion

Earn a professional certificate upon course completion

  • Globally recognized certificate
  • Verifiable online credential
  • Enhances professional credibility
certificate

Frequently Asked Questions

Looking for answers to other questions?

A vector database stores data in a format that enables similarity search, which is crucial for AI agents to retrieve relevant information from large datasets efficiently. It powers capabilities like semantic search in RAG and memory in AI agents.

You'll be introduced to vector databases (Weaviate) and Retrieval-Augmented Generation (RAG), and how they fit into the agentic AI ecosystem.

Yes, it discusses the role of both short-term (session-based) and long-term memory (persistent vector storage) in building effective agentic systems.

Yes, the course provides a certification upon completion.

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