1. What is a Vector Database?
A Vector Database stores and retrieves data based on meaning, not exact match.
Instead of storing plain text, it stores vectors (embeddings) and finds which ones are closest to your query vector.
Think of it as: Google for meaning
- It doesn’t care about the exact words, just the semantic similarity
2. Why Not Use a Regular Database?
A traditional SQL database is great for:
- Exact lookups
- Structured queries
But it can’t natively search for “things that are similar” in high-dimensional space.
Example:
- SQL can find “car” = “car”
- Vector DB can find “car” ≈ “automobile” ≈ “sedan”
3. How Does It Work?
Workflow:
- You create embeddings from your data (using an embedding model)
- Store them as vectors in the vector database
- When a user queries:
- Create an embedding for the query
- Database finds nearest vectors using similarity search
- Return related content
Similarity Search Methods:
- Cosine Similarity (angle between vectors)
- Euclidean Distance (straight-line distance)
- Dot Product (magnitude-based match)
4. Popular Vector Databases
- Pinecone → Fully managed, scalable
- Weaviate → Open-source + cloud options
- Milvus → Large-scale similarity search
- FAISS (Facebook AI Similarity Search) → Local, super fast
- Qdrant → Rust-based, blazing performance
5. Where Do Vector Databases Fit in AI?
They are the memory layer for your AI system.
Example in a Retrieval-Augmented Generation (RAG) pipeline:
- User Query → Create embedding
- Vector DB → Retrieve top-k similar documents
- LLM → Uses those docs to answer
This makes:
- Chatbots that remember
- AI search engines
- Context-aware assistants
- Recommendation systems
6. Key Questions
- Q: How do you measure similarity between embeddings?
A: Cosine similarity, Euclidean distance, dot product. - Q: Difference between FAISS and Pinecone?
A: FAISS is local/open-source, Pinecone is managed and scalable. - Q: Why use a Vector DB over relational DB?
A: Handles high-dimensional similarity search efficiently.
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