0:00
9:53
9:53

Vector Databases Explained: The Complete Guide for 2026

Tech

If you want to truly understand how AI applications like ChatGPT with memory, semantic search engines, and RAG systems actually work under the hood, this video is going to be one of the most important ones you watch this year. Everyone is talking about building AI agents. Everyone is talking about RAG. But almost nobody takes the time to explain the actual infrastructure that makes all of it possible. That infrastructure is the vector database. Traditional databases search for exact matches. AI applications need meaning-based matches. Vector databases were built to solve that problem, and once you understand what they do and why they exist, everything you’ve been learning about AI is going to click into place. In this video, I cover what vectors and embeddings are, how they capture semantic meaning, why RAG works like an open book exam, which vector database you should use (ChromaDB, Qdrant, Pinecone, Weaviate), and applications beyond RAG like recommendations and anomaly detection. One thing most people miss: how you chunk your documents before converting them into embeddings is just as important as the database you choose. I cover that too. Resources: Vector Databases • ChromaDB: https://www.trychroma.com/ • Qdrant: https://qdrant.tech/ • Pinecone: https://www.pinecone.io/ • Weaviate: https://weaviate.io/ • Milvus: https://milvus.io/ Embedding Models • OpenAI Embeddings: https://platform.openai.com/docs/guides/embeddings • Voyage AI: https://www.voyageai.com/ • Hugging Face Embeddings: https://huggingface.co/blog/getting-started-with-embeddings RAG Frameworks • LangChain: https://www.langchain.com/ • LlamaIndex: https://www.llamaindex.ai/ Agentic AI • The Gen Academy, Mastering Agentic AI Bootcamp: https://thegenacademy.com/ 00:00 – What Is a Vector Database? 00:44 – Who I Am 01:03 – The Problem 02:02 – What Is a Vector? 04:08 – RAG Explained 05:47 – Chunking Strategy 06:19 – Which Vector DB? 07:26 – Beyond RAG 08:19 – Closing​​​​​​​​​​​​​​​​ I am launching Mastering Agentic AI, a 6-week intensive, technical, and project-based bootcamp starting May 30th. And for my YouTube family, I am giving an exclusive 10% discount. Link is in the description. This is not just for software engineers and AI engineers. If you are an AI PM, a PMM, a go-to-market expert, or in any adjacent role building AI products, this is for you too. Being technical is no longer only an engineer's thing. Every week you will be working on real projects in two flavors: coding and SDK-based for engineers, and no-code or low-code for tech leads, PMs, and everyone else. https://maven.com/aishwarya-srinivasan/mastering-ai-agents?promoCode=EXCLUSIVE-YT

ADVERTISEMENT

Comments 96

Sign in to join the conversation

Sign in
R
raagini_dhar 6 days, 13 hours ago

Thank you

N
nadiaaether4 1 week, 2 days ago

Excellent explanation

B
bradley.page 1 week, 3 days ago

This is the most clear and clean and highest clarity explanation of vector database and RAG and all. Good work. Make more such videos and educate more people.

J
joanne.rose 1 week, 3 days ago

Great video overall—it prompted me to revisit and reflect on concepts that I use regularly. If I had one suggestion, it would be to spend more time on vector databases themselves: why they are such a breakthrough, how they enable efficient similarity search at scale, and why they have become a foundational building block for modern AI applications. The examples were useful, but I was hoping for a deeper intuition around the underlying value proposition and real-world impact. For me, the most compelling explanations are the ones that help people develop a mental model of why the technology matters, not just what it does. My one liner:”Embeddings are GPS coordinates for meaning, allowing AI to find related information even when different words are used”

B
bertrand_charpentier 1 week, 4 days ago

Nice explanation but why is the course price so high ? 2500 to 3000 dollars is huge for Indian participants.

B
bertrand_charpentier 2 weeks, 1 day ago

Great great explanation. Thank you.

S
steven.leon 2 weeks, 4 days ago

Nice video sister..what is the mathematics used to convert tokens to vectors?

J
justin_brown 2 weeks, 4 days ago

Loved your explanation!! Thank you!!

V
vanessa_carlson 2 weeks, 5 days ago

Very clear and precise information.

K
kevin.brown 3 weeks ago

2.5 Lkahs expensive course

joshuachen282
joshuachen282 3 weeks ago

Great presentation, Ash. Keep on trucking.

L
lorraine_powell 3 weeks, 3 days ago

Me who find this video while searching about pageIndex tutorial 😅😅😅

W
william_grant 3 weeks, 3 days ago

Ash. Superb job! Thanks a million

K
kamilly_sousa 3 weeks, 3 days ago

Very well explained, thanks

G
garry.hayes 3 weeks, 3 days ago

RAG has huge flaw ❌. if you put a book of businesses in vector DB and ask agent "give me top 3 businesses by annual revenue from RAG datasource". It will literally do semantic search "top 3 businesses". if the book does not have any such keywords, it cannot find the answer. Have you addressed this in any previous video?

A
aliciabloom16 3 weeks, 4 days ago

Excellent & precise!

C
christopher_thompson 3 weeks, 6 days ago

Solid overview, especially the chunking advice! A huge 'gotcha' for beginners is Dimensionality Mismatch. Different models produce different array lengths (e.g., 768 vs 1536). If your retriever and DB aren't using the exact same model, the search won't just be 'bad'—the database will likely throw a shape error and fail entirely.

S
sierrahayes217 3 weeks, 6 days ago

Video itself is a short great semantic vector and not a lengthy literal sequence. Excellent video.

D
damien_davies 4 weeks ago

Thank for explaining Vector DB very clearly Aishwarya Srinivasan

T
trinidad_apodaca 4 weeks, 1 day ago

U r a superstar in AI space.....AIsh=AI