1. Is a vector database the same as a relational database?

No, they are fundamentally different. A relational database stores data in tables and retrieves records using exact queries like SQL. A vector database stores numerical representations of data and retrieves results based on similarity. They serve different purposes and are often used together in AI systems.

2. Do I need to know machine learning to use a vector database?

You do not need a deep ML background to get started. Basic Python knowledge is enough to use tools like Chroma or Pinecone with pre-built embedding models. However, understanding embeddings and distance metrics will help you optimise performance and build more sophisticated applications over time.

3. Can a vector database store text, images, and audio together?

Yes. A vector database stores vectors regardless of the original data type. As long as you convert each data type into a numerical vector using the appropriate embedding model, you can store text, images, audio, and video in the same database and run cross-modal searches across them.

4. What is the difference between exact search and approximate search in vector databases?

Exact search checks every stored vector and returns the true nearest neighbours. It is accurate but very slow for large datasets. Approximate nearest neighbour (ANN) search uses indexing algorithms like HNSW to find results that are very close to the true nearest neighbours but much faster. Most production systems use ANN.

5. How many dimensions do vectors in a vector database typically have?

It depends on the embedding model used. Common embedding sizes range from 384 dimensions (Sentence-BERT small models) to 1,536 dimensions (OpenAI's text-embedding-3-small) and even 3,072 dimensions for larger models. More dimensions generally mean richer representations but also require more storage and compute.

6. Can I use a vector database with open-source LLMs?

Yes. Vector databases work with any embedding model and any LLM. You can pair Chroma or Qdrant with open-source models like Llama 3, Mistral, or Falcon. Frameworks like LangChain and LlamaIndex make it straightforward to connect vector databases with open-source LLMs for local or self-hosted AI applications.

7. What is the role of a vector database in a RAG pipeline?

In a retrieval-augmented generation pipeline, the vector database acts as the knowledge retrieval layer. When a user asks a question, the system converts the query to a vector, retrieves the most relevant documents from the vector database, and passes them as context to the language model. This grounds the model's response in real, specific information.

8. How do vector databases handle updates and deletions?

Most vector databases support upserts (insert or update) and deletes. However, because vector indexes are complex structures, deleting or updating vectors can sometimes require reindexing parts of the database. Tools like Pinecone and Weaviate handle this automatically, but it is something to consider when designing systems with frequently changing data.

9. Is pgvector a real alternative to dedicated vector databases?

pgvector is a good option for teams already using PostgreSQL who want to add vector search without introducing a new system. It works well for small to medium datasets. However, for high-scale production workloads with millions of vectors and low-latency requirements, dedicated vector databases like Qdrant or Pinecone significantly outperform pgvector in search speed and scalability.

10. Which industries are using vector databases the most?

Vector databases are widely adopted in tech, e-commerce, healthcare, finance, and media. In e-commerce, they power visual and semantic product search. In healthcare, they help retrieve similar patient records or medical literature. In finance, they support fraud detection. In media, they enable personalised content recommendations across streaming platforms.