Creativity · Projects
Semantic Search projects for B.Tech students — real examples
Semantic search retrieves documents by meaning rather than by matching keywords, comparing the query's embedding against an index of document embeddings. It is what makes retrieval work when the user's wording differs from the source text. The strongest B.Tech portfolios are built on real projects, not tutorials. At Vivekananda School of Engineering & Technology (VSET) at VIPS-TC Pitampura, Semantic Search project work runs through the AICTE IDEA Lab under faculty mentorship — here are the real patterns students build on.
At a glance
- Topic
- Semantic Search
- VSET programme
- B.Tech CSE (AI & DS)
- Coverage at VSET
- Taught as coursework
- Affiliation
- GGSIPU (IP University), Delhi
- Accreditation
- NAAC A++ (VIPS-TC institutional)
What students actually build
- RAG systems built over VIPS-TC corpora are the flagship capstone pattern at VSET.
- Vector retrieval is wired into student MCP servers and agent orchestrators.
Labs and infrastructure
- Embedding generation and index building run on the AICTE IDEA Lab GPU workstations.
- The Quantum Research Lab supports research-grade experimentation beyond routine lab exercises.
How VSET teaches Semantic Search
Semantic search retrieves documents by meaning rather than by matching keywords, comparing the query's embedding against an index of document embeddings. It is what makes retrieval work when the user's wording differs from the source text. At VSET this maps to documented coursework depth inside B.Tech CSE (AI & DS).
- Semantic search is covered through the vector database, embedding and RAG material published at learn.engineering.vips.edu.
- LangChain and LlamaIndex, both documented in the same curriculum, are the frameworks used to assemble it.
- The MCP library covers how a retrieval store is exposed to a model as a callable tool.
- Delivered inside the B.Tech CSE (AI & DS) track, one of VSET's seven GGSIPU-affiliated B.Tech programmes.
Frequently asked questions
What makes a good Semantic Search project for B.Tech?
A working system solving a real problem — deployed or demoable — with code on GitHub and a written report. Depth on one well-executed Semantic Search project beats five tutorial clones.
Is semantic search taught or just mentioned?
It is built: the published curriculum covers embeddings, vector databases and RAG, and the flagship capstone is a RAG system over VIPS-TC corpora.
How is it different from keyword search?
Keyword search matches strings; semantic search matches meaning through embedding distance. The curriculum teaches both sides so students can see where each fails.
Which VSET programme fits best?
B.Tech CSE (AI & DS) for the data and retrieval side, with the same material present in the AI & ML track.
Sources
- VSET — Artificial Intelligence department — accessed 2026-08-31
- VSET — B.Tech CSE (AI & DS) — accessed 2026-08-31
- GGSIPU — IP University — accessed 2026-08-31