Database / LanceDB Interview questions
What are the key features of LanceDB?
LanceDB combines several capabilities that are often split across separate tools in a typical AI retrieval stack, all built around one shared columnar table.
| Feature | What it Provides |
| Embedded architecture | Runs in-process, no separate server to deploy |
| Multimodal storage | Vectors, text, images, and other data live as columns in the same table |
| Vector indexing | ANN indices like IVF-PQ and HNSW for fast similarity search |
| Full-text search | BM25-based keyword search, combinable with vector search |
| Versioning | Every write creates a new version; supports checkout, restore, and tagging |
| Schema evolution | Add, rename, retype, or drop columns without rewriting the whole table |
Taken together, these features are what let a single LanceDB table serve as the retrieval layer for a RAG pipeline — storing the source documents, their embeddings, and their metadata together, then supporting semantic, keyword, and filtered queries against that same table.
What does LanceDB's versioning feature let you do?
What is combinable with vector search for hybrid retrieval?
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