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
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RAG pipeline : Data Ingestion line : Data -> Parsing -> Chunking -> Embedding -> Vector DB Retrievel line :User Query -> Embedding -> Semantic Search -> Retrieve Context -> LLM -> Answer
Algorithm got me at the right place at the right time. I am so thankful to you. Until now I only had a conceptual understanding of rag but not a clear picture on how to start. This week I am gonna excel a bit more on RAG. Thanks Ash. I mean it
Brief and simple explanation is the real winner which is why your videos comes to rescue. Thanks Aishwarya! Hope I get the opportunity to join thr Mastering Agentic AI bootcamp
the short introduction which establishes your credentials AND why I should listen to you was very nice of you. Thank you.
guys wtf, i ain't even in tech (i am doing philosophy phd) but now I want to learn more and buy your course 😭😭
You are such a wonderful explainer. Not too much. Not too little. Just the right amount of information. Thanks.
Hey Aish, thank you for the guidance. i feel your explanation is so relevant to the working professionals and it give motivation to learn new stuff. pls keep posted such content. thank you.
This is a powerful explanation in such a short time with proper intuition. Thank you.🙌🏼 A physicist is trying to break into the AI field here. 😅
Well content consumed in this morning
This is easily the best explanation of vector infrastructure I’ve seen in 2026. The way you broke down semantic search using the royalty example made the 'math' feel intuitive. As someone looking into AI for business automation, understanding the 'why' behind the retrieval step is a game-changer. Looking forward to more! Great SOUND as well BTW in this vedio..
Never heard of such a clean and brief explanation of vector database before. Thank you for recording this.👌
Very insightful. One quick question : Any reason why oracle 26ai is not mentioned in your list? Even if it does provide a native vector data type which rules out requiring a standalone vector db completely. They provide hybrid vector search too and Hybrid Vector Index is game changer along with automatic chunking strategy not only on the index but for RAG pipelines, recommendation engines or data preprocessing/AI data assembly line . In one of AI projects, I used Helidon, built RAG, utilized the TEQs, embedding models, Oracle 26ai vector data type to store those vectors, oracle gen ai with good LLM options- one ecosystem. So, Just curious on your thoughts, thank you Ash! Keep sharing these videos on important AI concepts especially how they unravel in production- super helpful ! ❤
DO ONE VIDEO ON VECTORLESS RAG TOO.
Let me add a point, Context it means the data or you can call it chunks which is retrieved from VectorDB & that context is passed as response from LLM
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.
Video itself is a short great semantic vector and not a lengthy literal sequence. Excellent video.
Thanks aishwarya for teaching me vector database i have my exam tomorrow and this video is a bigggggg help
Really interesting video, the explanation was clear and easy to follow 👍
A amazing lacture 🎉 , need a video on Docling by IBM.
Excellent tutorial 👍