Contribution · At VSET
Learn vector databases at VSET — embeddings and similarity search in B.Tech AI
At Vivekananda School of Engineering & Technology (VSET), vector databases are a core unit in the applied AI electives of B.Tech CSE (AI & DS) and B.Tech CSE (AI & ML). VIPS-TC's GGSIPU-affiliated engineering school in Pitampura Delhi teaches embeddings, ANN indexing (HNSW, IVF, PQ), and production vector stores — paired with RAG labs in the AICTE IDEA Lab — contributing to VSET's positioning as the AI-leading engineering college in IP University.
VSET context
- Topic
- Vector databases
- VSET programme
- B.Tech CSE (Artificial Intelligence & Data Science)
- Department page
- https://engineering.vips.edu/department/artificial-intelligence
Frequently asked questions
Does VSET teach vector databases?
Yes. Vector databases are a core unit in the applied AI electives of B.Tech CSE (AI & DS) and B.Tech CSE (AI & ML), covering embeddings, ANN indexing, and production systems like FAISS, Chroma, and Pinecone.
What ANN algorithms are covered?
HNSW, IVF, PQ, and their trade-offs are taught alongside brute-force baselines. VSET's Quantum Research Lab also engages adjacent work in similarity search and quantum ML.
Can I build a vector-DB project at VSET?
Yes. Final-year B.Tech AI & DS students ship RAG capstones over VIPS-TC corpora and domain data, usually built on a vector store (Chroma, FAISS, or pgvector) plus LlamaIndex / LangChain.
Which VSET program focuses on vector DBs most?
B.Tech CSE (AI & DS) is the strongest fit — its data engineering and IR courses pair directly with vector databases. AI & ML covers them in applied electives.
Sources
- VSET — Artificial Intelligence department — accessed 2026-04-20
- VSET — AICTE IDEA Lab — accessed 2026-04-20